21.8.3.4 Pancreatic Disease, CAT Analysis

Farag, A., Lu, L., Roth, H.R., Liu, J., Turkbey, E., Summers, R.M.,
A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling,
IP(26), No. 1, January 2017, pp. 386-399.
IEEE DOI 1612
biological organs BibRef

Wong, K.C.L., Summers, R.M., Kebebew, E., Yao, J.,
Pancreatic Tumor Growth Prediction With Elastic-Growth Decomposition, Image-Derived Motion, and FDM-FEM Coupling,
MedImg(36), No. 1, January 2017, pp. 111-123.
IEEE DOI 1701
Biological system modeling BibRef

Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., Gurusamy, K., Davidson, B., Pereira, S.P., Clarkson, M.J., Barratt, D.C.,
Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks,
MedImg(37), No. 8, August 2018, pp. 1822-1834.
IEEE DOI 1808
Image segmentation, Computed tomography, Liver, Kidney, Pancreas, Abdominal CT, segmentation, gallbladder BibRef

Balasubramanian, A.D.[Aruna Devi], Murugan, P.R.[Pallikonda Rajasekaran], Thiyagarajan, A.P.[Arun Prasath],
Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques,
IJIST(29), No. 4, 2019, pp. 399-418.
DOI Link 1911
ANN, GLCM features, image classification, magnetic resonance imaging (MRI), SVM BibRef

Man, Y., Huang, Y., Feng, J., Li, X., Wu, F.,
Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net,
MedImg(38), No. 8, August 2019, pp. 1971-1980.
IEEE DOI 1908
Pancreas, Image segmentation, Computed tomography, Feature extraction, deformable U-net BibRef

Hussein, S., Kandel, P., Bolan, C.W., Wallace, M.B., Bagci, U.,
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches,
MedImg(38), No. 8, August 2019, pp. 1777-1787.
IEEE DOI 1908
Lung, Unsupervised learning, Tumors, Cancer, Feature extraction, Deep learning, pancreatic cancer BibRef

Zhang, Y.L.[Yu-Ling], Wang, S.C.[Shu-Chang], Qu, S.Q.[Shu-Qiang], Zhang, H.L.[Hong-Li],
Support vector machine combined with magnetic resonance imaging for accurate diagnosis of paediatric pancreatic cancer,
IET-IPR(14), No. 7, 29 May 2020, pp. 1233-1239.
DOI Link 2005
BibRef

Xie, L., Yu, Q., Zhou, Y., Wang, Y., Fishman, E.K., Yuille, A.L.,
Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans,
MedImg(39), No. 2, February 2020, pp. 514-525.
IEEE DOI 2002
Pancreas, Image segmentation, Computed tomography, Biomedical imaging, Training, Neoplasms, Cancer, saliency transformation BibRef

Ning, Y.[Yang], Han, Z.Y.[Zhong-Yi], Zhong, L.[Li], Zhang, C.M.[Cai-Ming],
DRAN: Deep recurrent adversarial network for automated pancreas segmentation,
IET-IPR(14), No. 6, 11 May 2020, pp. 1091-1100.
DOI Link 2005
BibRef

Qiu, J.J.[Jia-Jun], Yin, J.[Jin], Qian, W.[Wei], Liu, J.H.[Jin-Heng], Huang, Z.X.[Zi-Xing], Yu, H.P.[Hao-Peng], Ji, L.[Lin], Zeng, X.X.[Xiao-Xi],
A Novel Multiresolution-Statistical Texture Analysis Architecture: Radiomics-Aided Diagnosis of PDAC Based on Plain CT Images,
MedImg(40), No. 1, January 2021, pp. 12-25.
IEEE DOI 2012
Pancreatic Ductal Adenocarcinoma. Computed tomography, Feature extraction, Multiresolution analysis, Gray-scale, Computer architecture, statistical analysis BibRef

Ahmed, R., Ye, J., Gerber, S.A., Linehan, D.C., Doyley, M.M.,
Preclinical Imaging Using Single Track Location Shear Wave Elastography: Monitoring the Progression of Murine Pancreatic Tumor Liver Metastasis In Vivo,
MedImg(39), No. 7, July 2020, pp. 2426-2439.
IEEE DOI 2007
Tumors, Mice, Liver, Ultrasonic imaging, Elastography, Shear wave elastography, preclinical imaging, respiration gating BibRef

Chen, X., Lin, X., Shen, Q., Qian, X.,
Combined Spiral Transformation and Model-Driven Multi-Modal Deep Learning Scheme for Automatic Prediction of TP53 Mutation in Pancreatic Cancer,
MedImg(40), No. 2, February 2021, pp. 735-747.
IEEE DOI 2102
Cancer, Deep learning, Tumors, Spirals, Correlation, Feature extraction, Spiral transformation, pancreatic cancer prediction BibRef

Zhang, D.W.[Ding-Wen], Zhang, J.J.[Jia-Jia], Zhang, Q.[Qiang], Han, J.G.[Jun-Gong], Zhang, S.[Shu], Han, J.W.[Jun-Wei],
Automatic pancreas segmentation based on lightweight DCNN modules and spatial prior propagation,
PR(114), 2021, pp. 107762.
Elsevier DOI 2103
Pancreas segmentation, Lightweight DCNN, Localization, Segmentation, Spatial prior BibRef

Xue, J., He, K., Nie, D., Adeli, E., Shi, Z., Lee, S.W., Zheng, Y., Liu, X., Li, D., Shen, D.,
Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation,
Cyber(51), No. 4, April 2021, pp. 2153-2165.
IEEE DOI 2103
Pancreas, Image segmentation, Computed tomography, Shape, Skeleton, Task analysis, Biomedical imaging, Multitask FCN, skeleton extraction BibRef

Wang, Y.[Yan], Tang, P.[Peng], Zhou, Y.Y.[Yu-Yin], Shen, W.[Wei], Fishman, E.K.[Elliot K.], Yuille, A.L.[Alan L.],
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction,
MedImg(40), No. 10, October 2021, pp. 2723-2735.
IEEE DOI 2110
Annotations, Image segmentation, Training data, Computed tomography, Training, Diseases, Cancer, Attention, medical image segmentation BibRef

Zhao, T.Y.[Tian-Yi], Cao, K.[Kai], Yao, J.W.[Jia-Wen], Nogues, I.[Isabella], Lu, L.[Le], Huang, L.Y.[Ling-Yun], Xiao, J.[Jing], Yin, Z.Z.[Zhao-Zheng], Zhang, L.[Ling],
3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management,
CVPR21(13738-13747)
IEEE DOI 2111
Geometry, Image segmentation, Computed tomography, Taxonomy, Imaging, Feature extraction BibRef

Chen, X.[Xiahan], Chen, Z.[Zihao], Li, J.[Jun], Zhang, Y.D.[Yu-Dong], Lin, X.Z.[Xiao-Zhu], Qian, X.H.[Xiao-Hua],
Model-Driven Deep Learning Method for Pancreatic Cancer Segmentation Based on Spiral-Transformation,
MedImg(41), No. 1, January 2022, pp. 75-87.
IEEE DOI 2201
Image segmentation, Cancer, Spirals, Deep learning, Solid modeling, Tumors, Spiral transformation, model-driven deep learning, pancreatic cancer segmentation BibRef

Ju, J.G.[Jian-Guo], Li, J.M.[Jia-Ming], Chang, Z.Q.[Zheng-Qi], Liang, Y.[Ying], Guan, Z.Y.[Zi-Yu], Xu, P.F.[Peng-Fei], Xie, F.[Fei], Wang, H.[Hexu],
Incorporating multi-stage spatial visual cues and active localization offset for pancreas segmentation,
PRL(170), 2023, pp. 85-92.
Elsevier DOI 2306
Pancreas segmentation, Coarse-to-fine, Active learning, Spacial context BibRef

Li, X.Y.[Xin-Yue], Guo, R.[Rui], Lu, J.[Jing], Chen, T.[Tao], Qian, X.H.[Xiao-Hua],
Causality-Driven Graph Neural Network for Early Diagnosis of Pancreatic Cancer in Non-Contrast Computerized Tomography,
MedImg(42), No. 6, June 2023, pp. 1656-1667.
IEEE DOI 2306
Feature extraction, Tumors, Computed tomography, Pancreatic cancer, Medical diagnostic imaging, Graph neural networks, multiple instance learning BibRef

Gotta, J.[Jennifer], Gruenewald, L.D.[Leon D.], Martin, S.S.[Simon S.], Booz, C.[Christian], Eichler, K.[Katrin], Mahmoudi, S.[Scherwin], Rezazadeh, C.Ö.[Canan Özdemir], Reschke, P.[Philipp], Biciusca, T.[Teodora], Juergens, L.J.[Lisa-Joy], Mader, C.[Christoph], Hammerstingl, R.[Renate], Sommer, C.M.[Christof M.], Vogl, T.J.[Thomas J.], Koch, V.[Vitali],
Unmasking pancreatic cancer: Advanced biomedical imaging for its detection in native versus arterial dual-energy computed tomography (DECT) scans,
IJIST(34), No. 2, 2024, pp. e23037.
DOI Link 2402
artificial intelligence, dual-energy computed tomography, machine learning, pancreatic cancer BibRef

Lakkshmanan, A.[Ajanthaa], Ananth, C.A.[C. Anbu], Tiroumalmouroughane, S.,
CNN Classification of Computed Tomography Images for Pancreatic Tumor Detection,
IJIG(24), No. 5, September 2024, pp. 2340001.
DOI Link 2410
BibRef

Zhou, B.[Bangyuan], Xin, G.J.[Guo-Jiang], Liang, H.[Hao], Ding, C.S.[Chang-Song],
SEY-Net: Semantic edge Y-shaped network for pancreas segmentation,
IET-IPR(18), No. 13, 2024, pp. 3950-3960.
DOI Link 2411
edge information extraction module, one-stage, pancreas segmentation, Y-Net BibRef

Tang, Y.[Yumou], Zhan, K.[Kun], Tian, Z.B.[Zhi-Bo], Zhang, M.X.[Ming-Xuan], Wang, S.S.[Sai-Sai], Wen, X.M.[Xue-Ming],
Curriculum Knowledge Switching for Pancreas Segmentation,
ICIP23(985-989)
IEEE DOI Code:
WWW Link. 2312
BibRef

Filho, A.R.G.[Arlindo R. Galvão], Wastowski, I.J.[Isabela Jubé], Moreira, M.A.R.[Marise A. R.], de P.C.-Cysneiros, M.A.[Maria A.], Coelho, C.J.[Clarimar José],
Pancreatic Cancer Detection Using Hyperspectral Imaging and Machine Learning,
ICIP23(2870-2874)
IEEE DOI 2312
BibRef

Li, J.[Ji], Chen, Y.R.[Yin-Ran], Chen, R.[Rong], Shen, D.F.[Dong-Fang], Luo, X.B.[Xiong-Biao],
3D End-to-End Boundary-Aware Networks for Pancreas Segmentation,
ICIP22(2031-2035)
IEEE DOI 2211
Image segmentation, Solid modeling, Shape, Computed tomography, Surgery, Pancreas segmentation, deep learning, reverse attention, 3D U-Net BibRef

Zhu, Z.[Zhuotun], Lu, Y.Y.[Yong-Yi], Shen, W.[Wei], Fishman, E.K.[Elliot K.], Yuille, A.L.[Alan L.],
Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors,
CVAMD21(3395-3401)
IEEE DOI 2112
Sensitivity, Computed tomography, Ducts, Neural networks, Pancreas BibRef

Yu, Q., Xie, L., Wang, Y., Zhou, Y., Fishman, E.K., Yuille, A.L.,
Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation,
CVPR18(8280-8289)
IEEE DOI 1812
Image segmentation, Pancreas, Computed tomography, Training, Testing, Biomedical imaging BibRef

Asaturyan, H.[Hykoush], Villarini, B.[Barbara],
Hierarchical Framework for Automatic Pancreas Segmentation in MRI Using Continuous Max-Flow and Min-Cuts Approach,
ICIAR18(562-570).
Springer DOI 1807
BibRef

Karasawa, K.[Ken'ichi], Kitasaka, T.[Takayuki], Oda, M.[Masahiro], Nimura, Y.[Yukitaka], Hayashi, Y.[Yuichiro], Fujiwara, M.[Michitaka], Misawa, K.[Kazunari], Rueckert, D.[Daniel], Mori, K.[Kensaku],
Structure Specific Atlas Generation and Its Application to Pancreas Segmentation from Contrasted Abdominal CT Volumes,
MCV15(47-56).
Springer DOI 1608
BibRef

Takagi, M.[Mikio], Sakaue, K.[Katsuhiko],
The Analysis of Moving Granules in a Pancreatic Cell by Digital Moving Image Processing,
ICPR78(735-739). BibRef 7800

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Tomographic Image Generation, CAT, CT, Reconstruction .