Document Type

Conference Paper

Publication Date

2023

DOI

10.1117/12.2655508

Publication Title

Medical Imaging 2023: Computer-Aided Diagnosis, Proceedings of SPIE 12465

Volume

12465

Pages

124650V (1-6)

Conference Name

SPIE Medical Imaging 2023: Computer Aided Diagnosis, February 19-24, 2023, San Diego, California

Abstract

Deep learning (DL)-based medical imaging and image segmentation algorithms achieve impressive performance on many benchmarks. Yet the efficacy of deep learning methods for future clinical applications may become questionable due to the lack of ability to reason with uncertainty and interpret probable areas of failures in prediction decisions. Therefore, it is desired that such a deep learning model for segmentation classification is able to reliably predict its confidence measure and map back to the original imaging cases to interpret the prediction decisions. In this work, uncertainty estimation for multiorgan segmentation task is evaluated to interpret the predictive modeling in DL solutions. We use the state-of-the-art nnU-Net to perform segmentation of 15 abdominal organs (spleen, right kidney, left kidney, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, pancreas, right adrenal gland, left adrenal gland, duodenum, bladder, prostate/uterus) using 200 patient cases for the Multimodality Abdominal Multi-Organ Segmentation Challenge 2022. Further, the softmax probabilities from different variants of nnU-Net are used to compute the knowledge uncertainty in the deep learning framework. Knowledge uncertainty from ensemble of DL models is utilized to quantify and visualize class activation map for two example segmented organs. The preliminary result of our model shows that class activation maps may be used to interpret the prediction decision made by the DL model used in this study.

Rights

Copyright 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).

One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

Original Publication Citation

Sadique, M. S., Farzana, W., Temtam, A., & Iftekharuddin, K. (2023). Class activation mapping and uncertainty estimation in multi-organ segmentation. In K. Iftekharuddin & W. Chen (Eds.), Medical Imaging 2023: Computer-Aided Diagnosis, Proceedings of SPIE 12465 (124650V). SPIE. https://doi.org/10.1117/12.2655508

ORCID

0000-0002-6734-6802 (Sadique), 0000-0003-1995-2426 (Farzana), 0000-0001-8316-4163 (Iftekharuddin)

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