An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation

• URL: http://arxiv.org/abs/2208.07417v1
• Date: Mon, 15 Aug 2022 19:40:18 GMT
• Title: An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation
• Authors: Abhishek Srivastava, Debesh Jha, Elif Keles, Bulent Aydogan, Mohamed Abazeed, Ulas Bagci
• Abstract summary: We propose a new OAR segmentation framework called OARFocalFuseNet. It fuses multi-scale features and employs focal modulation for capturing global-local context across multiple scales. Our best performing method (OARFocalFuseNet) obtained a dice coefficient of 0.7995 and hausdorff distance of 5.1435 on OpenKBP datasets.
• Score: 2.6770199357488242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning. Existing deep learning-based multi-scale fusion architectures have demonstrated a tremendous capacity for 2D medical image segmentation. The key to their success is aggregating global context and maintaining high resolution representations. However, when translated into 3D segmentation problems, existing multi-scale fusion architectures might underperform due to their heavy computation overhead and substantial data diet. To address this issue, we propose a new OAR segmentation framework, called OARFocalFuseNet, which fuses multi-scale features and employs focal modulation for capturing global-local context across multiple scales. Each resolution stream is enriched with features from different resolution scales, and multi-scale information is aggregated to model diverse contextual ranges. As a result, feature representations are further boosted. The comprehensive comparisons in our experimental setup with OAR segmentation as well as multi-organ segmentation show that our proposed OARFocalFuseNet outperforms the recent state-of-the-art methods on publicly available OpenKBP datasets and Synapse multi-organ segmentation. Both of the proposed methods (3D-MSF and OARFocalFuseNet) showed promising performance in terms of standard evaluation metrics. Our best performing method (OARFocalFuseNet) obtained a dice coefficient of 0.7995 and hausdorff distance of 5.1435 on OpenKBP datasets and dice coefficient of 0.8137 on Synapse multi-organ segmentation dataset.

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