Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study

• URL: http://arxiv.org/abs/2209.02126v1
• Date: Mon, 5 Sep 2022 20:20:19 GMT
• Title: Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study
• Authors: Sulaiman Vesal, Iani Gayo, Indrani Bhattacharya, Shyam Natarajan, Leonard S. Marks, Dean C Barratt, Richard E. Fan, Yipeng Hu, Geoffrey A. Sonn, and Mirabela Rusu
• Abstract summary: We introduce a novel 2.5D deep neural network for prostate segmentation on ultrasound images. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of $94.0pm0.03$ and Hausdorff Distance (HD95) of 2.28 $mm$ in an independent set of subjects.
• Score: 2.571022281023314
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of $94.0\pm0.03$ and Hausdorff Distance (HD95) of 2.28 $mm$ in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: $91.0\pm0.03$; HD95: 3.7$mm$ and Dice: $82.0\pm0.03$; HD95: 7.1 $mm$).

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