A Multi-Stage Framework for the 2022 Multi-Structure Segmentation for Renal Cancer Treatment
Yusheng Liu, Zhongchen Zhao, Lisheng Wang
TL;DR
This paper introduces a multi-stage framework utilizing a novel nnhra-unet network for automatic 3D segmentation of kidney and related structures in CTA images, aiding renal cancer surgery planning.
Contribution
It proposes a new nnhra-unet network and a multi-stage framework for improved multi-structure segmentation in renal cancer treatment.
Findings
Achieved effective segmentation of kidney and associated structures.
Participated successfully in the KiPA2022 challenge.
Demonstrated potential clinical benefits in surgical planning.
Abstract
Three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images is of great clinical significance. Automatic segmentation of kidney, renal tumor, renal vein and renal artery benefits a lot on surgery-based renal cancer treatment. In this paper, we propose a new nnhra-unet network, and use a multi-stage framework which is based on it to segment the multi-structure of kidney and participate in the KiPA2022 challenge.
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced X-ray and CT Imaging · Renal cell carcinoma treatment