Using Large Context for Kidney Multi-Structure Segmentation from CTA Images

Fuente: arXiv
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Main Authors: Cao, Weiwei, Cao, Yuzhu
Format: Preprint
Published: 2022
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author Cao, Weiwei
Cao, Yuzhu
author_facet Cao, Weiwei
Cao, Yuzhu
contents Accurate and automated segmentation of multi-structure (i.e., kidneys, renal tu-mors, arteries, and veins) from 3D CTA is one of the most important tasks for surgery-based renal cancer treatment (e.g., laparoscopic partial nephrectomy). This paper briefly presents the main technique details of the multi-structure seg-mentation method in MICCAI 2022 KIPA challenge. The main contribution of this paper is that we design the 3D UNet with the large context information cap-turing capability. Our method ranked eighth on the MICCAI 2022 KIPA chal-lenge open testing dataset with a mean position of 8.2. Our code and trained models are publicly available at https://github.com/fengjiejiejiejie/kipa22_nnunet.
format Preprint
id arxiv_https___arxiv_org_abs_2208_04525
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Using Large Context for Kidney Multi-Structure Segmentation from CTA Images
Cao, Weiwei
Cao, Yuzhu
Image and Video Processing
Computer Vision and Pattern Recognition
Accurate and automated segmentation of multi-structure (i.e., kidneys, renal tu-mors, arteries, and veins) from 3D CTA is one of the most important tasks for surgery-based renal cancer treatment (e.g., laparoscopic partial nephrectomy). This paper briefly presents the main technique details of the multi-structure seg-mentation method in MICCAI 2022 KIPA challenge. The main contribution of this paper is that we design the 3D UNet with the large context information cap-turing capability. Our method ranked eighth on the MICCAI 2022 KIPA chal-lenge open testing dataset with a mean position of 8.2. Our code and trained models are publicly available at https://github.com/fengjiejiejiejie/kipa22_nnunet.
title Using Large Context for Kidney Multi-Structure Segmentation from CTA Images
topic Image and Video Processing
Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2208.04525