Attention Guided Deep Supervision Model for Prostate Segmentation in Multisite Heterogeneous MRI Data

Kuruparan Shanmugalingam, Arcot Sowmya, Daniel Moses, Erik Meijering
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, PMLR 172:1085-1095, 2022.

Abstract

Prostate cancer and benign prostatic hyperplasia are common diseases in men and require early and accurate diagnosis for optimal treatment. Standard diagnostic tests such as the prostate-specific antigen test and digital rectal examination are inconvenient. Thus, non-invasive methods such as magnetic resonance imaging (MRI) and automated image analysis are increasingly utilised to facilitate and improve prostate diagnostics. Segmentation is a vital part of the prostate image analysis pipeline, and deep neural networks are now the tool of choice to automate this task. In this work, we benchmark various deep neural networks for 3D prostate segmentation using four different publicly available datasets and one private dataset. We show that popular networks such as U-Net trained on one dataset typically generalise poorly when tested on others due to data heterogeneity. Aiming to address this issue, we propose a novel deep-learning architecture for prostate whole-gland segmentation in T2-weighted MRI images that exploits various techniques such as pyramid pooling, concurrent spatial and channel squeeze and excitation, and deep supervision. Our extensive experiments demonstrate that it performs superiorly without requiring special adaptation to any specific dataset.

Cite this Paper

BibTeX
@InProceedings{pmlr-v172-shanmugalingam22a, title = {Attention Guided Deep Supervision Model for Prostate Segmentation in Multisite Heterogeneous MRI Data}, author = {Shanmugalingam, Kuruparan and Sowmya, Arcot and Moses, Daniel and Meijering, Erik}, booktitle = {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning}, pages = {1085--1095}, year = {2022}, editor = {Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi}, volume = {172}, series = {Proceedings of Machine Learning Research}, month = {06--08 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v172/shanmugalingam22a/shanmugalingam22a.pdf}, url = {https://proceedings.mlr.press/v172/shanmugalingam22a.html}, abstract = {Prostate cancer and benign prostatic hyperplasia are common diseases in men and require early and accurate diagnosis for optimal treatment. Standard diagnostic tests such as the prostate-specific antigen test and digital rectal examination are inconvenient. Thus, non-invasive methods such as magnetic resonance imaging (MRI) and automated image analysis are increasingly utilised to facilitate and improve prostate diagnostics. Segmentation is a vital part of the prostate image analysis pipeline, and deep neural networks are now the tool of choice to automate this task. In this work, we benchmark various deep neural networks for 3D prostate segmentation using four different publicly available datasets and one private dataset. We show that popular networks such as U-Net trained on one dataset typically generalise poorly when tested on others due to data heterogeneity. Aiming to address this issue, we propose a novel deep-learning architecture for prostate whole-gland segmentation in T2-weighted MRI images that exploits various techniques such as pyramid pooling, concurrent spatial and channel squeeze and excitation, and deep supervision. Our extensive experiments demonstrate that it performs superiorly without requiring special adaptation to any specific dataset.} }
Endnote
%0 Conference Paper %T Attention Guided Deep Supervision Model for Prostate Segmentation in Multisite Heterogeneous MRI Data %A Kuruparan Shanmugalingam %A Arcot Sowmya %A Daniel Moses %A Erik Meijering %B Proceedings of The 5th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2022 %E Ender Konukoglu %E Bjoern Menze %E Archana Venkataraman %E Christian Baumgartner %E Qi Dou %E Shadi Albarqouni %F pmlr-v172-shanmugalingam22a %I PMLR %P 1085--1095 %U https://proceedings.mlr.press/v172/shanmugalingam22a.html %V 172 %X Prostate cancer and benign prostatic hyperplasia are common diseases in men and require early and accurate diagnosis for optimal treatment. Standard diagnostic tests such as the prostate-specific antigen test and digital rectal examination are inconvenient. Thus, non-invasive methods such as magnetic resonance imaging (MRI) and automated image analysis are increasingly utilised to facilitate and improve prostate diagnostics. Segmentation is a vital part of the prostate image analysis pipeline, and deep neural networks are now the tool of choice to automate this task. In this work, we benchmark various deep neural networks for 3D prostate segmentation using four different publicly available datasets and one private dataset. We show that popular networks such as U-Net trained on one dataset typically generalise poorly when tested on others due to data heterogeneity. Aiming to address this issue, we propose a novel deep-learning architecture for prostate whole-gland segmentation in T2-weighted MRI images that exploits various techniques such as pyramid pooling, concurrent spatial and channel squeeze and excitation, and deep supervision. Our extensive experiments demonstrate that it performs superiorly without requiring special adaptation to any specific dataset.
APA
Shanmugalingam, K., Sowmya, A., Moses, D. & Meijering, E.. (2022). Attention Guided Deep Supervision Model for Prostate Segmentation in Multisite Heterogeneous MRI Data. Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 172:1085-1095 Available from https://proceedings.mlr.press/v172/shanmugalingam22a.html.

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