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

TL;DR

This paper introduces OARFocalFuseNet, a novel 3D organ segmentation network that effectively fuses multi-scale features and employs focal modulation to improve accuracy and efficiency in medical image segmentation tasks.

Contribution

The paper proposes a new 3D segmentation framework, OARFocalFuseNet, that enhances multi-scale feature fusion with focal modulation for better global-local context capturing.

Findings

Outperforms state-of-the-art methods on OpenKBP and Synapse datasets.

Achieves a dice coefficient of 0.7995 on OpenKBP.

Demonstrates promising results with Hausdorff distance of 5.1435.

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.