20.8.3.4 Pancreatic Disease, CAT Analysis

Chapter Contents (Back)
Pancreas. Pancreatic Disease.

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.[Junwei],
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


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 .


Last update:May 10, 2021 at 18:51:10