21.13.3 Medical Applications -- Prostate Cancer Analysis

Chapter Contents (Back)
Prostate Cancer. Cancer Detection.

Pathak, S.D., Haynor, D.R., Kim, Y.,
Edge-guided boundary delineation in prostate ultrasound images,
MedImg(19), No. 12, December 2000, pp. 1211-1219.
IEEE Top Reference. 0110
BibRef

Shen, D.G.[Ding-Gang], Zhan, Y.Q.[Yi-Qiang], Davatzikos, C.[Christos],
Segmentation of prostate boundaries from ultrasound images using statistical shape model,
MedImg(22), No. 4, April 2003, pp. 539-551.
IEEE Abstract. 0306
BibRef

Zhan, Y.Q.[Yi-Qiang], Shen, D.G.[Ding-Gang],
Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method,
MedImg(25), No. 3, March 2006, pp. 256-272.
IEEE DOI 0604
BibRef

Fei, B.W.[Bao-Wei], Duerk, J.L., Boll, D.T., Lewin, J.S., Wilson, D.L.,
Slice-to-Volume Registration and Its Potential Application to Interventional MRI-Guided Radio-Frequency Thermal Ablation of Prostate Cancer,
MedImg(22), No. 4, April 2003, pp. 515-525.
IEEE Abstract. 0306
BibRef

Gong, L., Pathak, S.D., Haynor, D.R., Cho, P.S., Kim, Y.,
Parametric Shape Modeling Using Deformable Superellipses for Prostate Segmentation,
MedImg(23), No. 3, March 2004, pp. 340-349.
IEEE Abstract. 0403
BibRef

Madabhushi, A., Feldman, M.D., Metaxas, D.N., Tomaszeweski, J., Chute, D.,
Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI,
MedImg(24), No. 12, December 2005, pp. 1611-1625.
IEEE DOI 0601
BibRef

Madabhushi, A.[Anant], Shi, J.B.[Jian-Bo], Feldman, M.D.[Michael D.], Rosen, M.[Mark], Tomaszewski, J.E.[John E.],
Comparing Ensembles of Learners: Detecting Prostate Cancer from High Resolution MRI,
CVAMIA06(25-36).
Springer DOI 0605
BibRef

Tahir, M.A.[Muhammad Atif], Bouridane, A.[Ahmed], Kurugollu, F.[Fatih], Amira, A.[Abbes],
A Novel Prostate Cancer Classification Technique Using Intermediate Memory Tabu Search,
JASP(2005), No. 14, 2005, pp. 2241-2249.
WWW Link. 0603
BibRef
Earlier:
Feature selection using tabu search for improving the classification rate of prostate needle biopsies,
ICPR04(II: 335-338).
IEEE DOI 0409
BibRef

Tahir, M.A.[Muhammad Atif], Bouridane, A.[Ahmed], Kurugollu, F.[Fatih],
Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier,
PRL(28), No. 4, 1 March 2007, pp. 438-446.
Elsevier DOI 0701
Tabu Search; K-NN classifier; Feature selection; Feature weighting; Prostate cancer diagnosis BibRef

Egorov, V., Ayrapetyan, S., Sarvazyan, A.P.,
Prostate Mechanical Imaging: 3-D Image Composition and Feature Calculations,
MedImg(25), No. 10, October 2006, pp. 1329-1340.
IEEE DOI 0609

See also Mechanical Imaging of the Breast. BibRef

Tutar, I.B., Pathak, S.D., Gong, L., Cho, P.S., Wallner, K., Kim, Y.,
Semiautomatic 3-D Prostate Segmentation from TRUS Images Using Spherical Harmonics,
MedImg(25), No. 12, December 2006, pp. 1645-1654.
IEEE DOI 0701
BibRef

Rieke, V., Kinsey, A.M., Ross, A.B., Nau, W.H., Diederich, C.J., Sommer, G., Pauly, K.B.,
Referenceless MR Thermometry for Monitoring Thermal Ablation in the Prostate,
MedImg(26), No. 6, June 2007, pp. 813-821.
IEEE DOI 0706
BibRef

Zhan, Y., Shen, D., Zeng, J., Sun, L., Fichtinger, G., Moul, J., Davatzikos, C.,
Targeted Prostate Biopsy Using Statistical Image Analysis,
MedImg(26), No. 6, June 2007, pp. 779-788.
IEEE DOI 0706
BibRef

Lee, J., Liu, X., Jain, A.K., Song, D.Y., Burdette, E.C., Prince, J.L., Fichtinger, G.,
Prostate Brachytherapy Seed Reconstruction With Gaussian Blurring and Optimal Coverage Cost,
MedImg(28), No. 12, December 2009, pp. 1955-1968.
IEEE DOI 0912

See also Ultrasound Elastography: A Dynamic Programming Approach. BibRef

Lee, J., Labat, C., Jain, A.K., Song, D.Y., Burdette, E.C., Fichtinger, G., Prince, J.L.,
REDMAPS: Reduced-Dimensionality Matching for Prostate Brachytherapy Seed Reconstruction,
MedImg(30), No. 1, January 2011, pp. 38-51.
IEEE DOI 1101
BibRef

Betrouni, N., Vermandel, M., Pasquier, D., Rousseau, J.,
Ultrasound image guided patient setup for prostate cancer conformal radiotherapy,
PRL(28), No. 13, 1 October 2007, pp. 1808-1817.
Elsevier DOI 0709
Ultrasound imaging; Prostate model; Segmentation; Optical stereolocalization; Conformal radiotherapy; Registration BibRef

Tabesh, A., Teverovskiy, M., Pang, H.Y., Kumar, V.P., Verbel, D., Kotsianti, A., Saidi, O.,
Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images,
MedImg(26), No. 10, October 2007, pp. 1366-1378.
IEEE DOI 0711
BibRef

Crouch, J.R., Pizer, S.M., Chaney, E.L., Hu, Y.C., Mageras, G.S., Zaider, M.,
Automated Finite-Element Analysis for Deformable Registration of Prostate Images,
MedImg(26), No. 10, October 2007, pp. 1379-1390.
IEEE DOI 0711
BibRef

Mohamed, S.S., Salama, M.M.A.,
Prostate Cancer Spectral Multifeature Analysis Using TRUS Images,
MedImg(27), No. 4, April 2008, pp. 548-556.
IEEE DOI 0804
BibRef

Mohamed, S.S., Youssef, A.M., El-Saadany, E.F., Salama, M.M.A.,
Prostate Tissue Characterization Using TRUS Image Spectral Features,
ICIAR06(II: 589-601).
Springer DOI 0610
BibRef
Earlier:
Artificial Life Feature Selection Techniques for Prostrate Cancer Diagnosis Using TRUS Images,
ICIAR05(903-913).
Springer DOI 0509
BibRef

Li, B.[Bing], Acton, S.T.[Scott T.],
Automatic Active Model Initialization via Poisson Inverse Gradient,
IP(17), No. 8, August 2008, pp. 1406-1420.
IEEE DOI 0808
BibRef

Li, B.[Bing], Patil, A.V.[Abhay V.], Hossack, J.A.[John A.], Acton, S.T.[Scott T.],
3D Segmentation of the Prostate via Poisson Inverse Gradient Initialization,
ICIP07(V: 25-28).
IEEE DOI 0709
BibRef

Shao, F.[Fan], Ling, K.V.[Keck Voon], Ng, W.S.[Wan Sing],
Automatic 3d Prostate Surface Detection From Trus With Level Sets,
IJIG(4), No. 3, July 2004, pp. 385-403. 0407
BibRef

Liu, X.[Xin], Langer, D.L., Haider, M.A., Yang, Y., Wernick, M.N.[Miles N.], Yetik, I.S.[Imam Samil],
Prostate Cancer Segmentation With Simultaneous Estimation of Markov Random Field Parameters and Class,
MedImg(28), No. 6, June 2009, pp. 906-915.
IEEE DOI 0906
Markov Random Field. BibRef

Liu, X.[Xin], Yetik, I.S.[Imam Samil],
A Maximum Likelihood Classification method for image segmentation considering subject variability,
Southwest10(125-128).
IEEE DOI 1005
BibRef

Liu, X.[Xin], Yetik, I.S.[Imam Samil], Wernick, M.N.[Miles N.],
Simultaneous estimation of the Markov random field parameters and the classes for image segmentation,
ICIP08(3048-3051).
IEEE DOI 0810
BibRef

Huang, P.W., Lee, C.H.,
Automatic Classification for Pathological Prostate Images Based on Fractal Analysis,
MedImg(28), No. 7, July 2009, pp. 1037-1050.
IEEE DOI 0906
BibRef

Maggio, S., Palladini, A., Marchi, L.D., Alessandrini, M., Speciale, N., Masetti, G.,
Predictive Deconvolution and Hybrid Feature Selection for Computer-Aided Detection of Prostate Cancer,
MedImg(29), No. 2, February 2010, pp. 455-464.
IEEE DOI 1002
BibRef

Artan, Y., Haider, M.A., Langer, D.L., van der Kwast, T.H., Evans, A.J., Yang, Y., Wernick, M.N., Trachtenberg, J., Yetik, I.S.,
Prostate Cancer Localization With Multispectral MRI Using Cost-Sensitive Support Vector Machines and Conditional Random Fields,
IP(19), No. 9, September 2010, pp. 2444-2455.
IEEE DOI 1008
BibRef

Gao, Y., Sandhu, R., Fichtinger, G., Tannenbaum, A.R.,
A Coupled Global Registration and Segmentation Framework With Application to Magnetic Resonance Prostate Imagery,
MedImg(29), No. 10, October 2010, pp. 1781-1794.
IEEE DOI 1011
BibRef

Garnier, C., Bellanger, J.J., Wu, K., Shu, H., Costet, N., Mathieu, R., de Crevoisier, R., Coatrieux, J.L.,
Prostate Segmentation in HIFU Therapy,
MedImg(30), No. 3, March 2011, pp. 792-803.
IEEE DOI 1103
BibRef

Kuenen, M.P.J., Mischi, M., Wijkstra, H.,
Contrast-Ultrasound Diffusion Imaging for Localization of Prostate Cancer,
MedImg(30), No. 8, August 2011, pp. 1493-1502.
IEEE DOI 1108
BibRef

Bouatmane, S.[Sabrina], Roula, M.A.[Mohamed Ali], Bouridane, A.[Ahmed], Al-Maadeed, S.[Somaya],
Round-Robin sequential forward selection algorithm for prostate cancer classification and diagnosis using multispectral imagery,
MVA(22), No. 5, September 2011, pp. 865-878.
WWW Link. 1108
BibRef

Hu, Y., Carter, T.J., Ahmed, H.U., Emberton, M., Allen, C., Hawkes, D.J., Barratt, D.C.,
Modelling Prostate Motion for Data Fusion During Image-Guided Interventions,
MedImg(30), No. 11, November 2011, pp. 1887-1900.
IEEE DOI 1111
BibRef

Lobo, J.R., Moradi, M., Chng, N., Dehghan, E., Morris, W.J., Fichtinger, G., Salcudean, S.E.,
Use of Needle Track Detection to Quantify the Displacement of Stranded Seeds Following Prostate Brachytherapy,
MedImg(31), No. 3, March 2012, pp. 738-748.
IEEE DOI 1203
BibRef

Nguyen, K.[Kien], Sabata, B.[Bikash], Jain, A.K.[Anil K.],
Prostate cancer grading: Gland segmentation and structural features,
PRL(33), No. 7, 1 May 2012, pp. 951-961.
Elsevier DOI 1203
Prostate cancer; Benign; Carcinoma; Gleason grading system; Gland segmentation; Nuclei From the ICPR paper. BibRef

Nguyen, K.[Kien], Jain, A.K.[Anil K.], Allen, R.L.[Ronald L.],
Automated Gland Segmentation and Classification for Gleason Grading of Prostate Tissue Images,
ICPR10(1497-1500).
IEEE DOI 1008
Award, ICPR. BibRef

Liao, S.[Shu], Shen, D.G.[Ding-Gang],
A Feature-Based Learning Framework for Accurate Prostate Localization in CT Images,
IP(21), No. 8, August 2012, pp. 3546-3559.
IEEE DOI 1208
BibRef

Shi, Y.H.[Yong-Hong], Liao, S.[Shu], Shen, D.G.[Ding-Gang],
Learning Statistical Correlation of Prostate Deformations for Fast Registration,
MLMI11(1-9).
Springer DOI 1109
BibRef

Toth, R., Madabhushi, A.,
Multifeature Landmark-Free Active Appearance Models: Application to Prostate MRI Segmentation,
MedImg(31), No. 8, August 2012, pp. 1638-1650.
IEEE DOI 1208
BibRef

Toth, R.[Robert], Ribault, J.[Justin], Gentile, J.[John], Sperling, D.[Dan], Madabhushi, A.[Anant],
Simultaneous segmentation of prostatic zones using Active Appearance Models with multiple coupled levelsets,
CVIU(117), No. 9, 2013, pp. 1051-1060.
Elsevier DOI 1307
Active Appearance Models BibRef

Sparks, R.[Rachel], Madabhushi, A.[Anant],
Statistical shape model for manifold regularization: Gleason grading of prostate histology,
CVIU(117), No. 9, 2013, pp. 1138-1146.
Elsevier DOI 1307
Manifold learning BibRef

Chandra, S.S., Dowling, J.A., Shen, K.K.[Kai-Kai], Raniga, P., Pluim, J.P.W., Greer, P.B., Salvado, O., Fripp, J.,
Patient Specific Prostate Segmentation in 3-D Magnetic Resonance Images,
MedImg(31), No. 10, October 2012, pp. 1955-1964.
IEEE DOI 1210
BibRef
Earlier: A1, A2, A3, A5, A6, A7, A8, Only:
Automatic Segmentation of the Prostate in 3D Magnetic Resonance Images Using Case Specific Deformable Models,
DICTA11(7-12).
IEEE DOI 1205
BibRef

Mahdavi, S.S., Moradi, M., Morris, W.J., Goldenberg, S.L., Salcudean, S.E.,
Fusion of Ultrasound B-Mode and Vibro-Elastography Images for Automatic 3-D Segmentation of the Prostate,
MedImg(31), No. 11, November 2012, pp. 2073-2082.
IEEE DOI 1211
BibRef

Han, E.J.[Eun Ji], Lee, S.H.[Sang-Hoon], Sohn, H.S.[Hyung Sun], Chung, Y.A.[Yong-An], Chung, S.K.[Soo Kyo],
Assessment of testicular uptake in flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography,
IJIST(22), No. 4, December 2012, pp. 245-249.
DOI Link 1211
BibRef

Atupelage, C.[Chamidu], Nagahashi, H.[Hiroshi], Yamaguchi, M.[Masahiro], Abe, T.[Tokiya], Hashiguchi, A.[Akinori], Sakamoto, M.[Michiie],
Classification of Prostate Histopathology Images Based on Multifractal Analysis,
IEICE(E95-D), No. 12, December 2012, pp. 3037-3045.
WWW Link. 1212
BibRef

Liao, S., Gao, Y., Lian, J., Shen, D.,
Sparse Patch-Based Label Propagation for Accurate Prostate Localization in CT Images,
MedImg(32), No. 2, February 2013, pp. 419-434.
IEEE DOI 1301
BibRef

Nir, G., Sahebjavaher, R.S., Kozlowski, P., Chang, S.D., Sinkus, R., Goldenberg, S.L., Salcudean, S.E.,
Model-Based Registration of Ex Vivo and In Vivo MRI of the Prostate Using Elastography*,
MedImg(32), No. 7, 2013, pp. 1349-1361.
IEEE DOI 1307
BibRef
Earlier: Earlier version had author error. MedImg(32), No. 6, 2013, pp. 1068-1080.
IEEE DOI 1307
cancer; deformation; ex vivo prostate MRI BibRef

Nir, G., Sahebjavaher, R.S., Kozlowski, P., Chang, S.D., Jones, E.C., Goldenberg, S.L., Salcudean, S.E.,
Registration of Whole-Mount Histology and Volumetric Imaging of the Prostate Using Particle Filtering,
MedImg(33), No. 8, August 2014, pp. 1601-1613.
IEEE DOI 1408
Image segmentation BibRef

Gavrilovic, M., Azar, J.C., Lindblad, J., Wahlby, C., Bengtsson, E., Busch, C., Carlbom, I.B.,
Blind Color Decomposition of Histological Images,
MedImg(32), No. 6, 2013, pp. 983-994.
IEEE DOI 1307
blind source separation; blind color decomposition; image restoration; prostate BibRef

Gorelick, L., Veksler, O., Gaed, M., Gomez, J.A., Moussa, M., Bauman, G., Fenster, A., Ward, A.D.,
Prostate Histopathology: Learning Tissue Component Histograms for Cancer Detection and Classification,
MedImg(32), No. 10, 2013, pp. 1804-1818.
IEEE DOI 1311
cancer BibRef

Artan, Y., Oto, A., Yetik, I.S.,
Cross-Device Automated Prostate Cancer Localization With Multiparametric MRI,
IP(22), No. 12, 2013, pp. 5385-5394.
IEEE DOI 1312
biomedical MRI BibRef

Gao, Y.Z.[Yao-Zong], Zhan, Y.Q.[Yi-Qiang], Shen, D.G.[Ding-Gang],
Incremental Learning With Selective Memory (ILSM): Towards Fast Prostate Localization for Image Guided Radiotherapy,
MedImg(33), No. 2, February 2014, pp. 518-534.
IEEE DOI 1403
biological organs BibRef

Niaf, E., Flamary, R., Rouviere, O., Lartizien, C., Canu, S.,
Kernel-Based Learning From Both Qualitative and Quantitative Labels: Application to Prostate Cancer Diagnosis Based on Multiparametric MR Imaging,
IP(23), No. 3, March 2014, pp. 979-991.
IEEE DOI 1403
biological tissues BibRef

Qiu, W., Yuan, J., Ukwatta, E., Sun, Y., Rajchl, M., Fenster, A.,
Prostate Segmentation: An Efficient Convex Optimization Approach With Axial Symmetry Using 3-D TRUS and MR Images,
MedImg(33), No. 4, April 2014, pp. 947-960.
IEEE DOI 1404
Biopsy BibRef

Sun, Y., Yuan, J., Qiu, W., Rajchl, M., Romagnoli, C., Fenster, A.,
Three-Dimensional Nonrigid MR-TRUS Registration Using Dual Optimization,
MedImg(34), No. 5, May 2015, pp. 1085-1095.
IEEE DOI 1505
Biopsy BibRef

Yuan, J.[Jing], Qiu, W.[Wu], Rajchl, M.[Martin], Ukwatta, E.[Eranga], Tai, X.C.[Xue-Cheng], Fenster, A.[Aaron],
Efficient 3D Endfiring TRUS Prostate Segmentation with Globally Optimized Rotational Symmetry,
CVPR13(2211-2218)
IEEE DOI 1309
3D TRUS; Continuous Max-Flow; Convex Optimization; Image Segmentation BibRef

Nguyen, K., Sarkar, A., Jain, A.K.,
Prostate Cancer Grading: Use of Graph Cut and Spatial Arrangement of Nuclei,
MedImg(33), No. 12, December 2014, pp. 2254-2270.
IEEE DOI 1412
biomedical optical imaging BibRef

Lee, G., Singanamalli, A., Wang, H.B.[Hai-Bo], Feldman, M.D., Master, S.R., Shih, N.N.C., Spangler, E., Rebbeck, T., Tomaszewski, J.E., Madabhushi, A.,
Supervised Multi-View Canonical Correlation Analysis (sMVCCA): Integrating Histologic and Proteomic Features for Predicting Recurrent Prostate Cancer,
MedImg(34), No. 1, January 2015, pp. 284-297.
IEEE DOI 1502
biomedical MRI BibRef

Pääkkönen, J.[Jouni], Päivinen, N.[Niina], Nykänen, M.[Matti], Paavonen, T.[Timo],
An automated gland segmentation and classification method in prostate biopsies: an image source-independent approach,
MVA(26), No. 1, January 2015, pp. 103-113.
Springer DOI 1503
BibRef

Nouranian, S., Mahdavi, S.S., Spadinger, I., Morris, W.J., Salcudean, S.E., Abolmaesumi, P.,
A Multi-Atlas-Based Segmentation Framework for Prostate Brachytherapy,
MedImg(34), No. 4, April 2015, pp. 950-961.
IEEE DOI 1504
Brachytherapy BibRef

Nouranian, S., Ramezani, M., Spadinger, I., Morris, W.J., Salcudean, S.E., Abolmaesumi, P.,
Learning-Based Multi-Label Segmentation of Transrectal Ultrasound Images for Prostate Brachytherapy,
MedImg(35), No. 3, March 2016, pp. 921-932.
IEEE DOI 1603
Brachytherapy BibRef

Wu, P.F.[Peng-Fei], Liu, Y.G.[Yi-Guang], Li, Y.Z.[Yong-Zhong], Liu, B.B.[Bing-Bing],
Robust Prostate Segmentation Using Intrinsic Properties of TRUS Images,
MedImg(34), No. 6, June 2015, pp. 1321-1335.
IEEE DOI 1506
biological organs BibRef

Shi, Y.H.[Ying-Huan], Gao, Y.Z.[Yao-Zong], Liao, S.[Shu], Zhang, D.Q.[Dao-Qiang], Gao, Y.[Yang], Shen, D.G.[Ding-Gang],
Semi-Automatic Segmentation of Prostate in CT Images via Coupled Feature Representation and Spatial-Constrained Transductive Lasso,
PAMI(37), No. 11, November 2015, pp. 2286-2303.
IEEE DOI 1511
computerised tomography BibRef

Imani, F., Abolmaesumi, P., Gibson, E., Khojaste, A., Gaed, M., Moussa, M., Gomez, J.A., Romagnoli, C., Leveridge, M., Chang, S., Siemens, D.R., Fenster, A., Ward, A.D., Mousavi, P.,
Computer-Aided Prostate Cancer Detection Using Ultrasound RF Time Series: In Vivo Feasibility Study,
MedImg(34), No. 11, November 2015, pp. 2248-2257.
IEEE DOI 1511
Biopsy BibRef

Khallaghi, S., Sanchez, C.A., Rasoulian, A., Sun, Y., Imani, F., Khojaste, A., Goksel, O., Romagnoli, C., Abdi, H., Chang, S., Mousavi, P., Fenster, A., Ward, A., Fels, S., Abolmaesumi, P.,
Biomechanically Constrained Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions,
MedImg(34), No. 11, November 2015, pp. 2404-2414.
IEEE DOI 1511
Biomechanics BibRef

Khallaghi, S., Sanchez, C.A., Rasoulian, A., Nouranian, S., Romagnoli, C., Abdi, H., Chang, S.D., Black, P.C., Goldenberg, L., Morris, W.J., Spadinger, I., Fenster, A., Ward, A., Fels, S., Abolmaesumi, P.,
Statistical Biomechanical Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions,
MedImg(34), No. 12, December 2015, pp. 2535-2549.
IEEE DOI 1601
biological organs BibRef

Wang, Y., Cheng, J.Z., Ni, D., Lin, M., Qin, J., Luo, X., Xu, M., Xie, X., Heng, P.A.,
Towards Personalized Statistical Deformable Model and Hybrid Point Matching for Robust MR-TRUS Registration,
MedImg(35), No. 2, February 2016, pp. 589-604.
IEEE DOI 1602
Biological system modeling BibRef

Tian, Z., Liu, L., Zhang, Z., Fei, B.,
Superpixel-Based Segmentation for 3D Prostate MR Images,
MedImg(35), No. 3, March 2016, pp. 791-801.
IEEE DOI 1603
Active contours BibRef

Guo, Y., Gao, Y., Shen, D.,
Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching,
MedImg(35), No. 4, April 2016, pp. 1077-1089.
IEEE DOI 1604
Biomedical imaging BibRef

Gao, Y., Shao, Y., Lian, J., Wang, A.Z., Chen, R.C., Shen, D.,
Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests,
MedImg(35), No. 6, June 2016, pp. 1532-1543.
IEEE DOI 1606
Bladder BibRef

Tang, S., Chen, J., Samant, P., Stratton, K., Xiang, L.,
Transurethral Photoacoustic Endoscopy for Prostate Cancer: A Simulation Study,
MedImg(35), No. 7, July 2016, pp. 1780-1787.
IEEE DOI 1608
biomedical optical imaging BibRef

Mahara, A., Khan, S., Murphy, E.K., Schned, A.R., Hyams, E.S., Halter, R.J.,
3D Microendoscopic Electrical Impedance Tomography for Margin Assessment During Robot-Assisted Laparoscopic Prostatectomy,
MedImg(34), No. 7, July 2015, pp. 1590-1601.
IEEE DOI 1507
Cancer BibRef

Khan, S., Mahara, A., Hyams, E.S., Schned, A.R., Halter, R.J.,
Prostate Cancer Detection Using Composite Impedance Metric,
MedImg(35), No. 12, December 2016, pp. 2513-2523.
IEEE DOI 1612
Cancer BibRef

van Sloun, R.J.G.[Ruud J.G.], Demi, L.[Libertario], Postema, A.W.[Arnoud W.], de la Rosette, J.J.M.C.H.[Jean J.M.C.H.], Wijkstra, H.[Hessel], Mischi, M.[Massimo],
Entropy of Ultrasound-Contrast-Agent Velocity Fields for Angiogenesis Imaging in Prostate Cancer,
MedImg(36), No. 3, March 2017, pp. 826-837.
IEEE DOI 1703
Blood BibRef

Zhang, J., Liu, M., Shen, D.,
Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks,
IP(26), No. 10, October 2017, pp. 4753-4764.
IEEE DOI 1708
biomedical MRI, computerised tomography, feedforward neural nets, learning (artificial intelligence), medical image processing, object detection, regression analysis, 3D T1-weighted magnetic resonance images, 3D computed tomography images, CNN based regression model, Biological neural networks, Biomedical imaging, Machine learning, BibRef

Wang, Z., Liu, C., Cheng, D., Wang, L., Yang, X., Cheng, K.T.,
Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network,
MedImg(37), No. 5, May 2018, pp. 1127-1139.
IEEE DOI 1805
Cancer, Feature extraction, Lesions, Manuals, Neural networks, Principal component analysis, CS PCa detection, neural network BibRef

Salimi, A.[Ahad], Pourmina, M.A.[Mohammad Ali], Moin, M.S.[Mohammad-Shahram],
Fully automatic prostate segmentation in MR images using a new hybrid active contour-based approach,
SIViP(12), No. 8, November 2018, pp. 1629-1637.
Springer DOI 1809
BibRef

Wildeboer, R.R., van Sloun, R.J.G., Schalk, S.G., Mannaerts, C.K., van der Linden, J.C., Huang, P., Wijkstra, H., Mischi, M.,
Convective-Dispersion Modeling in 3D Contrast-Ultrasound Imaging for the Localization of Prostate Cancer,
MedImg(37), No. 12, December 2018, pp. 2593-2602.
IEEE DOI 1812
Principal component analysis, Dispersion, Kinetic theory, Imaging, 3D BibRef

Azizi, S., Bayat, S., Yan, P., Tahmasebi, A., Kwak, J.T., Xu, S., Turkbey, B., Choyke, P., Pinto, P., Wood, B., Mousavi, P., Abolmaesumi, P.,
Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound,
MedImg(37), No. 12, December 2018, pp. 2695-2703.
IEEE DOI 1812
Ultrasonic imaging, Biopsy, Logic gates, Recurrent neural networks, Principal component analysis, Prostate cancer, cancer detection BibRef

Moradi, H., Tang, S., Salcudean, S.E.,
Toward Intra-Operative Prostate Photoacoustic Imaging: Configuration Evaluation and Implementation Using the da Vinci Research Kit,
MedImg(38), No. 1, January 2019, pp. 57-68.
IEEE DOI 1901
Transducers, Robots, Surgery, Probes, Image reconstruction, Tomography, Photoacoustic tomography, prostate cancer, limited view BibRef

He, K., Cao, X., Shi, Y., Nie, D., Gao, Y., Shen, D.,
Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks,
MedImg(38), No. 2, February 2019, pp. 585-595.
IEEE DOI 1902
Image segmentation, Computed tomography, Shape, Bladder, Task analysis, Robustness, Image segmentation, neural networks, prostate cancer BibRef

Li, W., Li, J., Sarma, K.V., Ho, K.C., Shen, S., Knudsen, B.S., Gertych, A., Arnold, C.W.,
Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images,
MedImg(38), No. 4, April 2019, pp. 945-954.
IEEE DOI 1904
Feature extraction, Image segmentation, Biomedical imaging, Glands, Prostate cancer, Solid modeling, Computer-aided diagnosis (CAD), region-based convolutional neural networks (R-CNN) BibRef

Cao, R., Bajgiran, A.M.[A. Mohammadian], Mirak, S.A.[S. Afshari], Shakeri, S., Zhong, X., Enzmann, D., Raman, S., Sung, K.,
Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet,
MedImg(38), No. 11, November 2019, pp. 2496-2506.
IEEE DOI 1911
Lesions, Principal component analysis, Magnetic resonance imaging, Biopsy, Training, Encoding, convolutional neural network BibRef

Wang, Y., Dou, H., Hu, X., Zhu, L., Yang, X., Xu, M., Qin, J., Heng, P., Wang, T., Ni, D.,
Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound,
MedImg(38), No. 12, December 2019, pp. 2768-2778.
IEEE DOI 1912
Image segmentation, Shape, Ultrasonic imaging, Biomedical imaging, transrectal ultrasound BibRef

Jia, H., Xia, Y., Song, Y., Zhang, D., Huang, H., Zhang, Y., Cai, W.,
3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images,
MedImg(39), No. 2, February 2020, pp. 447-457.
IEEE DOI 2002
Image segmentation, Spatial resolution, Decoding, Convolution, Glands, magnetic resonance imaging BibRef

Zhu, Q., Du, B., Yan, P.,
Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation,
MedImg(39), No. 3, March 2020, pp. 753-763.
IEEE DOI 2004
Image segmentation, Biomedical imaging, Feature extraction, Neural networks, Shape, Training data, Adaptive systems, boundary-weighted loss BibRef

Wang, S., Nie, D., Qu, L., Shao, Y., Lian, J., Wang, Q., Shen, D.,
CT Male Pelvic Organ Segmentation via Hybrid Loss Network With Incomplete Annotation,
MedImg(39), No. 6, June 2020, pp. 2151-2162.
IEEE DOI 2006
Image segmentation, male pelvic organ, deep learning, incomplete annotation, CT BibRef

Liu, Q., Dou, Q., Yu, L., Heng, P.A.,
MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data,
MedImg(39), No. 9, September 2020, pp. 2713-2724.
IEEE DOI 2009
Magnetic resonance imaging, Image segmentation, Training, Robustness, Data models, Biomedical imaging, Task analysis, knowledge transfer BibRef

Shao, Y., Wang, J., Wodlinger, B., Salcudean, S.E.,
Improving Prostate Cancer (PCa) Classification Performance by Using Three-Player Minimax Game to Reduce Data Source Heterogeneity,
MedImg(39), No. 10, October 2020, pp. 3148-3158.
IEEE DOI 2010
Principal component analysis, Feature extraction, Biopsy, Cancer, Radio frequency, Data source heterogeneity, BibRef

Wang, S., Liu, M., Lian, J., Shen, D.,
Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy,
MedImg(40), No. 1, January 2021, pp. 310-320.
IEEE DOI 2012
Image segmentation, Computed tomography, Feature extraction, Bladder, Proposals, Image coding, Image segmentation, CT image BibRef

Rossi, A., Hosseinzadeh, M., Bianchini, M., Scarselli, F., Huisman, H.,
Multi-Modal Siamese Network for Diagnostically Similar Lesion Retrieval in Prostate MRI,
MedImg(40), No. 3, March 2021, pp. 986-995.
IEEE DOI 2103
Feature extraction, Cancer, Magnetic resonance imaging, Neural networks, Lesions, Task analysis, Prostate cancer, lesion similarity BibRef

Shen, T.C.[Tian-Cheng], Yang, Y.[Yibo], Lin, Z.C.[Zhou-Chen], Zhang, M.[Mingbin],
Recurrent learning with clique structures for prostate sparse-view CT artifacts reduction,
IET-IPR(15), No. 3, 2021, pp. 648-655.
DOI Link 2106
BibRef

Pinckaers, H.[Hans], Bulten, W.[Wouter], van der Laak, J.[Jeroen], Litjens, G.[Geert],
Detection of Prostate Cancer in Whole-Slide Images Through End-to-End Training With Image-Level Labels,
MedImg(40), No. 7, July 2021, pp. 1817-1826.
IEEE DOI 2107
Training, Prostate cancer, Pathology, Biomedical imaging, Biological system modeling, Task analysis, Streaming media, prostate cancer BibRef

He, K.[Kelei], Lian, C.F.[Chun-Feng], Zhang, B.[Bing], Zhang, X.[Xin], Cao, X.H.[Xiao-Huan], Nie, D.[Dong], Gao, Y.[Yang], Zhang, J.F.[Jun-Feng], Shen, D.G.[Ding-Gang],
HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation in CT Images,
MedImg(40), No. 8, August 2021, pp. 2118-2128.
IEEE DOI 2108
Task analysis, Image segmentation, Computed tomography, Deformable models, Biomedical imaging, Computer architecture, consistency learning BibRef

Jia, H.Z.[Hao-Zhe], Cai, W.D.[Wei-Dong], Huang, H.[Heng], Xia, Y.[Yong],
Learning multi-scale synergic discriminative features for prostate image segmentation,
PR(126), 2022, pp. 108556.
Elsevier DOI 2204
Prostate segmentation, Intra-class consistency, Inter-class discrimination, Synergic multi-task loss BibRef

Xu, X.A.[Xuan-Ang], Sanford, T.[Thomas], Turkbey, B.[Baris], Xu, S.[Sheng], Wood, B.J.[Bradford J.], Yan, P.K.[Ping-Kun],
Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation,
MedImg(41), No. 6, June 2022, pp. 1331-1345.
IEEE DOI 2206
Image segmentation, Ultrasonic imaging, Training, Feature extraction, Biomedical imaging, Imaging, shadow artifact BibRef

Peng, T.[Tao], Zhao, J.[Jing], Gu, Y.D.[Yi-Dong], Wang, C.S.[Cai-Shan], Wu, Y.Y.[Yi-Yun], Cheng, X.X.[Xiu-Xiu], Cai, J.[Jing],
H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve,
PR(131), 2022, pp. 108890.
Elsevier DOI 2208
Accurate prostate segmentation, Transrectal ultrasound, Principal curve, Optimized closed polygonal segment method, Interpretable mathematical model BibRef

Peng, T.[Tao], Tang, C.Y.[Cai-Yin], Wu, Y.Y.[Yi-Yun], Cai, J.[Jing],
H-SegMed: A Hybrid Method for Prostate Segmentation in TRUS Images via Improved Closed Principal Curve and Improved Enhanced Machine Learning,
IJCV(130), No. 8, August 2022, pp. 1896-1919.
Springer DOI 2207
BibRef

Moradi, H.[Hamid], Foruzan, A.H.[Amir Hossein],
Integration of Dynamic Multi-Atlas and Deep Learning Techniques to Improve Segmentation of the Prostate in MR Images,
IJIG(22), No. 4, July 2022, pp. 2250031.
DOI Link 2208
BibRef

Yang, Q.[Qianye], Atkinson, D.[David], Fu, Y.[Yunguan], Syer, T.[Tom], Yan, W.[Wen], Punwani, S.[Shonit], Clarkson, M.J.[Matthew J.], Barratt, D.C.[Dean C.], Vercauteren, T.[Tom], Hu, Y.P.[Yi-Peng],
Cross-Modality Image Registration Using a Training-Time Privileged Third Modality,
MedImg(41), No. 11, November 2022, pp. 3421-3431.
IEEE DOI 2211
Training, Imaging, Task analysis, Medical diagnostic imaging, Image registration, Prostate cancer, Magnetic resonance imaging, multi-parametric MRI BibRef

Juneja, M.[Mamta], Saini, S.K.[Sumindar Kaur], Acharjee, R.[Rajarshi], Kaul, S.[Sambhav], Thakur, N.[Niharika], Jindal, P.[Prashant],
PC-SNet for automated detection of prostate cancer in multiparametric-magnetic resonance imaging,
IJIST(32), No. 6, 2022, pp. 1861-1879.
DOI Link 2212
convolution neural network, magnetic resonance imaging, prostate cancer, segmentation BibRef

Chen, Z.[Zhen], Yang, C.[Chen], Zhu, M.[Meilu], Peng, Z.[Zhe], Yuan, Y.X.[Yi-Xuan],
Personalized Retrogress-Resilient Federated Learning Toward Imbalanced Medical Data,
MedImg(41), No. 12, December 2022, pp. 3663-3674.
IEEE DOI 2212
Servers, Training, Medical diagnostic imaging, Computational modeling, Prototypes, Collaborative work, prostate segmentation BibRef

Hung, A.L.Y.[Alex Ling Yu], Zheng, H.X.[Hao-Xin], Miao, Q.[Qi], Raman, S.S.[Steven S.], Terzopoulos, D.[Demetri], Sung, K.[Kyunghyun],
CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI,
MedImg(42), No. 1, January 2023, pp. 291-303.
IEEE DOI 2301
Image segmentation, Transformers, Magnetic resonance imaging, Standards, Image resolution, Decoding, Attention mechanism, transformer network BibRef

Baum, Z.M.C.[Zachary M. C.], Hu, Y.P.[Yi-Peng], Barratt, D.C.[Dean C.],
Meta-Learning Initializations for Interactive Medical Image Registration,
MedImg(42), No. 3, March 2023, pp. 823-833.
IEEE DOI 2303
Image registration, Biomedical imaging, Training, Annotations, Ultrasonic imaging, Task analysis, prostate cancer BibRef

Wang, F.[Fan], Xu, X.[Xuanang], Yang, D.[Defu], Chen, R.C.[Ronald C.], Royce, T.J.[Trevor J.], Wang, A.[Andrew], Lian, J.[Jun], Lian, C.F.[Chun-Feng],
Dynamic Cross-Task Representation Adaptation for Clinical Targets Co-Segmentation in CT Image-Guided Post-Prostatectomy Radiotherapy,
MedImg(42), No. 4, April 2023, pp. 1046-1055.
IEEE DOI 2304
Image segmentation, Task analysis, Computed tomography, Multitasking, Radiation therapy, Planning, Learning systems, computed tomography BibRef

Aleef, T.A.[Tajwar Abrar], Lobo, J.[Julio], Baghani, A.[Ali], Mohammed, S.[Shahed], Eskandari, H.[Hani], Moradi, H.[Hamid], Rohling, R.[Robert], Goldenberg, S.L.[S. Larry], Morris, W.J.[W. James], Mahdavi, S.S.[S. Sara], Salcudean, S.E.[Septimiu E.],
Multi-Frequency 3D Shear Wave Absolute Vibro-Elastography (S-WAVE) System for the Prostate,
MedImg(42), No. 11, November 2023, pp. 3436-3450.
IEEE DOI 2311
BibRef

Karthik, R., Menaka, R., Siddharth, M.V., Hussain, S.[Sameeha], Siddharth, P., Won, D.[Daehan],
HMARNET: A Hierarchical Multi-Attention Residual Network for Gleason scoring of prostate cancer,
IJIST(34), No. 1, 2024, pp. e22976.
DOI Link 2401
attention, deep learning, histopathology, prostate cancer, TCGA-PRAD BibRef


Carloni, G.[Gianluca], Pachetti, E.[Eva], Colantonio, S.[Sara],
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI,
CVAMD23(2608-2616)
IEEE DOI 2401
BibRef

Balaha, H.M.[Hossam Magdy], Ayyad, S.M.[Sarah M.], Alksas, A.[Ahmed], Elsorougy, A.[Ali], Badawy, M.A.[Mohamed Ali], Shehata, M.[Mohamed], El-Ghar, M.A.[Mohamed Abou], Ghazal, M.[Mohammed], Mahmoud, A.[Ali], Contractor, S.[Sohail], El-Baz, A.[Ayman],
Early Diagnosis of Prostate Cancer Using Parametric Estimation of IVIM from DW-MRI,
ICIP23(2910-2914)
IEEE DOI 2312
BibRef

Hammouda, K., Khalifa, F., Ghazal, M., Darwish, H.E., Yousaf, J., El-Baz, A.,
A Pyramidal CNN-Based Gleason Grading System Using Digitized Prostate Biopsy Specimens,
ICPR22(4277-4284)
IEEE DOI 2212
Deep learning, Measurement, Convolution, Biopsy, Neural networks, Convolutional neural networks, Prostate cancer BibRef

del Rio, M.[Mauro], Lianas, L.[Luca], Aspegren, O.[Oskar], Busonera, G.[Giovanni], Versaci, F.[Francesco], Zelic, R.[Renata], Vincent, P.H.[Per H.], Leo, S.[Simone], Pettersson, A.[Andreas], Akre, O.[Olof], Pireddu, L.[Luca],
AI Support for Accelerating Histopathological Slide Examinations of Prostate Cancer in Clinical Studies,
DeepHealth22(545-556).
Springer DOI 2208
BibRef

Pachetti, E.[Eva], Colantonio, S.[Sara], Pascali, M.A.[Maria Antonietta],
On the Effectiveness of 3D Vision Transformers for the Prediction of Prostate Cancer Aggressiveness,
MEDXF22(317-328).
Springer DOI 2208
BibRef

Peng, T.[Tao], Tang, C.Y.[Cai-Yin], Wang, J.[Jing],
Prostate Segmentation of Ultrasound Images Based on Interpretable-Guided Mathematical Model,
MMMod22(I:166-177).
Springer DOI 2203
BibRef

Deng, H.C.[Han-Chen], Cai, N.[Naxin], Peng, Y.H.[Ya-Hui],
Semi-Quantitative Analysis of DCE-MRI for Classification of the Prostate with and without Cancer,
ICIVC21(181-185)
IEEE DOI 2112
Support vector machines, Training, Sensitivity, Receivers, Linear discriminant analysis, Data mining, Prostate cancer, classification BibRef

Dhrangadhariya, A.[Anjani], Otálora, S.[Sebastian], Atzori, M.[Manfredo], Müller, H.[Henning],
Classification of Noisy Free-Text Prostate Cancer Pathology Reports Using Natural Language Processing,
AIDP20(154-166).
Springer DOI 2103
BibRef

Rossi, A.[Alberto], Bianchini, M.[Monica], Scarselli, F.[Franco],
Robust Prostate Cancer Classification with Siamese Neural Networks,
ISVC20(II:180-189).
Springer DOI 2103
BibRef

Lyons, A.[Alexander], Rossi, A.[Alberto],
Prostate MRI Registration Using Siamese Metric Learning,
ISVC20(II:593-603).
Springer DOI 2103
BibRef

Kalapahar, A., Silva-Rodríguez, J., Colomer, A., López-Mir, F., Naranjo, V.,
Gleason Grading of Histology Prostate Images Through Semantic Segmentation via Residual U-Net,
ICIP20(2501-2505)
IEEE DOI 2011
Image segmentation, Semantics, Biopsy, Task analysis, Prostate cancer, Feature extraction, Prostate Cancer, Histology, Residual U-Net BibRef

Cao, R., Zhong, X., Scalzo, F., Raman, S., Sung, K.,
Prostate Cancer Inference via Weakly-Supervised Learning using a Large Collection of Negative MRI,
VRMI19(434-439)
IEEE DOI 2004
Magnetic resonance imaging, Principal component analysis, Lesions, Cancer, Training, Testing, Cancer Inference, Prostate MRI BibRef

Reda, I., Ghazal, M., Shalaby, A., Elmogy, M., Aboulfotouh, A., El-Ghar, M.A., El-Melegy, M., Khalil, A., Keynton, R., El-Baz, A.,
Detecting and Localizing Prostate Cancer from Diffusion-Weighted Magnetic Resonance Imaging,
ICIP19(1405-1409)
IEEE DOI 1910
Prostate cancer, computer-aided diagnosis, convolutional neural network BibRef

Firjani, A., Khalifa, F., Elnakib, A., Gimel'farb, G.L., El-Ghar, M.A.[M. Abou], Elmaghraby, A., El-Baz, A.,
A novel image-based approach for early detection of prostate cancer,
ICIP12(2849-2852).
IEEE DOI 1302
BibRef
Earlier:
3D automatic approach for precise segmentation of the prostate from Diffusion-Weighted Magnetic Resonance Imaging,
ICIP11(2285-2288).
IEEE DOI 1201
BibRef

Germanese, D.[Danila], Colantonio, S.[Sara], Caudai, C.[Claudia], Pascali, M.A.[Maria Antonietta], Barucci, A.[Andrea], Zoppetti, N.[Nicola], Agostini, S.[Simone], Bertelli, E.[Elena], Mercatelli, L.[Laura], Miele, V.[Vittorio], Carpi, R.[Roberto],
May Radiomic Data Predict Prostate Cancer Aggressiveness?,
CAIPWS19(65-75).
Springer DOI 1909
BibRef

Alkadi, R.[Ruba], El-Baz, A.[Ayman], Taher, F.[Fatma], Werghi, N.[Naoufel],
A 2.5D Deep Learning-Based Approach for Prostate Cancer Detection on T2-Weighted Magnetic Resonance Imaging,
WiCV-E18(IV:734-739).
Springer DOI 1905
BibRef

Wang, Y., Zheng, B., Gao, D., Wang, J.,
Fully convolutional neural networks for prostate cancer detection using multi-parametric magnetic resonance images: an initial investigation,
ICPR18(3814-3819)
IEEE DOI 1812
Tumors, Image segmentation, Prostate cancer, Training, Magnetic resonance imaging, Feature extraction, Deep learning, Prostate cancer detection BibRef

Jit, D., Qian, J., Yu, J., Kurihara, T., Zhan, S.,
Automatic Prostate Segmentation on MR Images Using Enhanced Holistically-Nested Networks,
ICPR18(3820-3825)
IEEE DOI 1812
Image segmentation, Task analysis, Feature extraction, Biomedical imaging, Image edge detection, Training, Computer architecture BibRef

Reda, I., Ghazal, M., Shalaby, A., Elmogy, M., AbouEl-Fetouh, A., Ayinde, B.O., AbouEl-Ghar, M., Elmaghraby, A., Keynton, R., El-Baz, A.,
A Novel ADCs-Based CNN Classification System for Precise Diagnosis of Prostate Cancer,
ICPR18(3923-3928)
IEEE DOI 1812
Prostate cancer, Magnetic resonance imaging, Feature extraction, Solid modeling, Blood, Image color analysis, Prostate Cancer, Convolutional Neural Networks BibRef

Esteban, Á.E., Colomer, A., Naranjo, V., Sales, M.Á.,
Granulometry-Based Descriptor for Pathological Tissue Discrimination in Histopathological Images,
ICIP18(1413-1417)
IEEE DOI 1809
Pathology, Glands, Feature extraction, Prostate cancer, Image segmentation, Image reconstruction, Granulometry, histology BibRef

Elmahdy, M.S.[Mohamed S.], Jagt, T.[Thyrza], Yousefi, S.[Sahar], Sokooti, H.[Hessam], Zinkstok, R.[Roel], Hoogeman, M.[Mischa], Staring, M.[Marius],
Evaluation of Multi-metric Registration for Online Adaptive Proton Therapy of Prostate Cancer,
WBIR18(94-104).
Springer DOI 1806
BibRef

Sguario Coelho de Andrade, M.L., Skeika, E., Kaminski Aires, S.B.,
Segmentation of the Prostate Gland in Images Using Prior Knowledge and Level Set Method,
WVC17(31-36)
IEEE DOI 1804
biological organs, biomedical MRI, cancer, diseases, image segmentation, medical image processing, active shape model, prior-knowledge BibRef

Ghasab, M.A.J., Paplinski, A.P., Betts, J.M., Reynolds, H.M., Haworth, A.,
Automatic 3D modelling for prostate cancer brachytherapy,
ICIP17(4452-4456)
IEEE DOI 1803
Active appearance model, Image segmentation, Mathematical model, Shape, Solid modeling, Training, Visualisation BibRef

Mun, J., Jang, W.D., Sung, D.J., Kim, C.S.,
Comparison of objective functions in CNN-based prostate magnetic resonance image segmentation,
ICIP17(3859-3863)
IEEE DOI 1803
Entropy, Hamming distance, Image segmentation, Indexes, Linear programming, Training, prostate segmentation BibRef

Cho, C., Lee, Y.H., Lee, S.,
Prostate detection and segmentation based on convolutional neural network and topological derivative,
ICIP17(3071-3074)
IEEE DOI 1803
Convolutional neural networks, Databases, Image segmentation, Magnetic resonance imaging, Medical diagnostic imaging, Training, Topological Derivative BibRef

Albayrak, N.B.[Nur Banu], Yildirim, E.[Emrah], Akgul, Y.S.[Yusuf Sinan],
Prostate Size Inference from Abdominal Ultrasound Images with Patch Based Prior Information,
ACIVS17(249-259).
Springer DOI 1712
BibRef

Karimi, A.H.[Amir-Hossein], Chung, A.G.[Audrey G.], Shafiee, M.J.[Mohammad Javad], Khalvati, F.[Farzad], Haider, M.A.[Masoom A.], Ghodsi, A.[Ali], Wong, A.[Alexander],
Discovery Radiomics via a Mixture of Deep ConvNet Sequencers for Multi-parametric MRI Prostate Cancer Classification,
ICIAR17(45-53).
Springer DOI 1706
BibRef

Clark, T.[Tyler], Wong, A.[Alexander], Haider, M.A.[Masoom A.], Khalvati, F.[Farzad],
Fully Deep Convolutional Neural Networks for Segmentation of the Prostate Gland in Diffusion-Weighted MR Images,
ICIAR17(97-104).
Springer DOI 1706
BibRef

Zhang, J.J.[Jun-Jie], Baig, S.[Sameer], Wong, A.[Alexander], Haider, M.A.[Masoom A.], Khalvati, F.[Farzad],
Segmentation of Prostate in Diffusion MR Images via Clustering,
ICIAR17(471-478).
Springer DOI 1706
BibRef

Jensen, C.[Carina], Korsager, A.S.[Anne Sofie], Boesen, L.[Lars], Řstergaard, L.R.[Lasse Riis], Carl, J.[Jesper],
Computer Aided Detection of Prostate Cancer on Biparametric MRI Using a Quadratic Discriminant Model,
SCIA17(I: 161-171).
Springer DOI 1706
BibRef

Perez, I.M., Toivonen, J., Movahedi, P., Merisaari, H., Pesola, M., Taimen, P., Boström, P.J., Kiviniemi, A., Aronen, H.J., Pahikkala, T., Jambor, I.,
Diffusion weighted imaging of prostate cancer: Prediction of cancer using texture features from parametric maps of the monoexponential and kurtosis functions,
IPTA16(1-6)
IEEE DOI 1703
Gabor filters BibRef

Guinin, M., Ruan, S., Dubray, B., Massoptier, L., Gardin, I.,
Feature selection and patch-based segmentation in MRI for prostate radiotherapy,
ICIP16(2663-2667)
IEEE DOI 1610
Dictionaries BibRef

Sammouda, R., Aboalsamh, H., Saeed, F.,
Comparison between K mean and fuzzy C-mean methods for segmentation of near infrared fluorescent image for diagnosing prostate cancer,
ICCVIA15(1-6)
IEEE DOI 1603
biomedical optical imaging BibRef

Bojorquez, J.Z.[Jorge Zavala], Bricq, S.[Stephanie], Walker, P.M.[Paul M.], Lalande, A.[Alain],
Automatic classification of tissues using T1 and T2 relaxation times from prostate MRI: A step towards generation of PET/MR attenuation map,
ICIP15(1185-1189)
IEEE DOI 1512
MRI BibRef

Albayrak, N.B.[Nur Banu], Oktay, A.B.[Ayse Betul], Akgul, Y.S.[Yusuf Sinan],
Prostate detection from abdominal ultrasound images: A part based approach,
ICIP15(1955-1959)
IEEE DOI 1512
Abdominal Ultrasound; Graphical Model; HOG; Prostate Detection; SVM BibRef

Mahapatra, D.[Dwarikanath], Buhmann, J.M.[Joachim M.],
Visual Saliency Based Active Learning for Prostate MRI Segmentation,
MLMI15(9-16).
Springer DOI 1511
BibRef

Weingant, M.[Michaela], Reynolds, H.M.[Hayley M.], Haworth, A.[Annette], Mitchell, C.[Catherine], Williams, S.[Scott], DiFranco, M.D.[Matthew D.],
Ensemble Prostate Tumor Classification in H&E Whole Slide Imaging via Stain Normalization and Cell Density Estimation,
MLMI15(280-287).
Springer DOI 1511
BibRef

Chung, A.G.[Audrey G.], Scharfenberger, C.[Christian], Khalvati, F.[Farzad], Wong, A.[Alexander], Haider, M.A.[Masoom A.],
Statistical Textural Distinctiveness in Multi-Parametric Prostate MRI for Suspicious Region Detection,
ICIAR15(368-376).
Springer DOI 1507
BibRef

Samarasinghe, G., Sowmya, A., Moses, D.A.,
A Semi-Quantitative Analysis Model with Parabolic Modelling for DCE-MRI Sequences of Prostate,
DICTA14(1-6)
IEEE DOI 1502
biomedical MRI BibRef

Lehaire, J.[Jerome], Flamary, R.[Remi], Rouviere, O.[Olivier], Lartizien, C.[Carole],
Computer-aided diagnostic system for prostate cancer detection and characterization combining learned dictionaries and supervised classification,
ICIP14(2251-2255)
IEEE DOI 1502
Bismuth BibRef

Niaf, E.[Emilie], Flamary, R.[Remi], Rakotomamonjy, A.[Alain], Rouviere, O.[Olivier], Lartizien, C.[Carole],
SVM with feature selection and smooth prediction in images: Application to CAD of prostate cancer,
ICIP14(2246-2250)
IEEE DOI 1502
Biomedical imaging BibRef

Gao, Q.Q.[Qin-Quan], Asthana, A.[Akshay], Tong, T.[Tong], Hu, Y.P.[Yi-Peng], Rueckert, D.[Daniel], Edwards, P.[Philip],
Hybrid Decision Forests for Prostate Segmentation in Multi-channel MR Images,
ICPR14(3298-3303)
IEEE DOI 1412
Feature extraction BibRef

McCarthy, N.[Nicholas], Cunningham, P.[Padraig], OHurley, G.[Gillian],
The Contribution of Morphological Features in the Classification of Prostate Carcinoma in Digital Pathology Images,
ICPR14(3269-3273)
IEEE DOI 1412
Accuracy BibRef

Jacobs, J.G.[Joseph G.], Panagiotaki, E.[Eleftheria], Alexander, D.C.[Daniel C.],
Gleason Grading of Prostate Tumours with Max-Margin Conditional Random Fields,
MLMI14(85-92).
Springer DOI 1410
BibRef

Gao, Y.Z.[Yao-Zong], Shen, D.G.[Ding-Gang],
Context-Aware Anatomical Landmark Detection: Application to Deformable Model Initialization in Prostate CT Images,
MLMI14(165-173).
Springer DOI 1410
BibRef

Park, S.H.[Sang Hyun], Gao, Y.Z.[Yao-Zong], Shi, Y.H.[Ying-Huan], Shen, D.G.[Ding-Gang],
Interactive Prostate Segmentation Based on Adaptive Feature Selection and Manifold Regularization,
MLMI14(264-271).
Springer DOI 1410
BibRef

Qian, C.[Chunjun], Wang, L.[Li], Yousuf, A.[Ambereen], Oto, A.[Aytekin], Shen, D.G.[Ding-Gang],
In Vivo MRI Based Prostate Cancer Identification with Random Forests and Auto-context Model,
MLMI14(314-322).
Springer DOI 1410
BibRef

Shao, Y.Q.[Ye-Qin], Gao, Y.Z.[Yao-Zong], Yang, X.[Xin], Shen, D.G.[Ding-Gang],
CT Prostate Deformable Segmentation by Boundary Regression,
MCV14(127-136).
Springer DOI 1501
BibRef

Gao, Y.Z.[Yao-Zong], Wang, L.[Li], Shao, Y.Q.[Ye-Qin], Shen, D.G.[Ding-Gang],
Learning Distance Transform for Boundary Detection and Deformable Segmentation in CT Prostate Images,
MLMI14(93-100).
Springer DOI 1410
BibRef

Cazoulat, G., Simon, A., Dumenil, A., Gnep, K., de Crevoisier, R., Acosta, O., Haigron, P.,
Surface-Constrained Nonrigid Registration for Dose Monitoring in Prostate Cancer Radiotherapy,
MedImg(33), No. 7, July 2014, pp. 1464-1474.
IEEE DOI 1407
Accuracy BibRef

Wu, Y., Liu, G., Huang, M., Guo, J., Jiang, J., Yang, W., Chen, W., Feng, Q.,
Prostate Segmentation Based on Variant Scale Patch and Local Independent Projection,
MedImg(33), No. 6, June 2014, pp. 1290-1303.
IEEE DOI 1407
Computed tomography BibRef

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Automated Segmentation of Prostate MR Images Using Prior Knowledge Enhanced Random Walker,
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Cervical Cancer Analysis, Ovarian Cancer .


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