3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation
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Objective: Segmentation of colorectal cancerous regions from the Magnetic Resonance (MR) image is a crucial procedure for radiotherapy, which requires to accurately delineate boundaries of the tumors. This work aims to address this important while challenging task in an accurate as well as efficient manner. Methods: We propose a novel multi-tasking framework, referred to as 3D RoI-aware U-Net (3D RU-Net), for RoI localization and intra-RoI segmentation, where the two tasks share one backbone network. With the region proposals from the localization branch, we crop multi-level feature maps from the backbone network to form a U-Net-like intra-RoI segmentation branch. To effectively train the model, we propose a novel Dice based hybrid loss to tackle the issue of class-imbalance under the multi-task setting. Furthermore, we design a multi-resolution model ensemble strategy to improve the discrimination capability of the framework. Results: Our method has been validated on 64 cancerous cases with a four-fold cross-validation, outperforming state-of-the-art methods by a significant margin in terms of both accuracy and speed. Conclusion: Experimental results demonstrated that the proposed method enables accurate and fast whole volume RoI localization and intra-RoI segmentation. Significance: This paper proposes a general 3D segmentation framework which rapidly locates the RoI region in large volumetric images and accurately segments the in-region targets. The method has a great potential to be extended to other small 3D object segmentation tasks from medical images.
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