no code implementations • 26 Mar 2024 • Yongrui Yu, HanYu Chen, Zitian Zhang, Qiong Xiao, Wenhui Lei, Linrui Dai, Yu Fu, Hui Tan, Guan Wang, Peng Gao, Xiaofan Zhang
To address these problems, we present a pipeline that integrates the conditional diffusion model for lymph node generation and the nnU-Net model for lymph node segmentation to improve the segmentation performance of abdominal lymph nodes through synthesizing a diversity of realistic abdominal lymph node data.
1 code implementation • 1 Jul 2023 • Linrui Dai, Wenhui Lei, Xiaofan Zhang
One feasible way to reduce the cost is to annotate with coarse-grained superclass labels while using limited fine-grained annotations as a complement.
1 code implementation • 26 Jun 2023 • Wenhui Lei, Xu Wei, Xiaofan Zhang, Kang Li, Shaoting Zhang
Our findings are twofold: 1) MedLAM is capable of directly localizing any anatomical structure using just a few template scans, yet its performance surpasses that of fully supervised models; 2) MedLSAM not only aligns closely with the performance of SAM and its specialized medical adaptations with manual prompts but achieves this with minimal reliance on extreme point annotations across the entire dataset.
1 code implementation • 22 Nov 2022 • Ran Gu, Guotai Wang, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Yinan Chen, Wenjun Liao, Shichuan Zhang, Kang Li, Dimitris N. Metaxas, Shaoting Zhang
First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image.
1 code implementation • 18 Aug 2022 • Ran Gu, Jingyang Zhang, Guotai Wang, Wenhui Lei, Tao Song, Xiaofan Zhang, Kang Li, Shaoting Zhang
To solve this problem, we propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA) that adapts a model to segment similar structures in a target domain, which requires only limited annotations in the target domain by leveraging a set of existing annotated images of similar structures in a source domain.
1 code implementation • 7 Jun 2022 • Hao Fu, Guotai Wang, Wenhui Lei, Wei Xu, Qianfei Zhao, Shichuan Zhang, Kang Li, Shaoting Zhang
Accurate segmentation of Anatomical brain Barriers to Cancer spread (ABCs) plays an important role for automatic delineation of Clinical Target Volume (CTV) of brain tumors in radiotherapy.
1 code implementation • 13 May 2022 • Ran Gu, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Xiaofan Zhang, Guotai Wang, Shaoting Zhang
To tackle this deficiency, we propose Contrastive Domain Disentangle (CDD) network for generalizable medical image segmentation.
1 code implementation • 21 Nov 2021 • Wenhui Lei, Qi Su, Ran Gu, Na Wang, Xinglong Liu, Guotai Wang, Xiaofan Zhang, Shaoting Zhang
Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation.
1 code implementation • 18 Sep 2021 • Ran Gu, Jingyang Zhang, Rui Huang, Wenhui Lei, Guotai Wang, Shaoting Zhang
First, we present a domain composition method that represents one certain domain by a linear combination of a set of basis representations (i. e., a representation bank).
1 code implementation • 3 Feb 2021 • Wenhui Lei, Haochen Mei, Zhengwentai Sun, Shan Ye, Ran Gu, Huan Wang, Rui Huang, Shichuan Zhang, Shaoting Zhang, Guotai Wang
Despite the stateof-the-art performance achieved by Convolutional Neural Networks (CNNs) for automatic segmentation of OARs, existing methods do not provide uncertainty estimation of the segmentation results for treatment planning, and their accuracy is still limited by several factors, including the low contrast of soft tissues in CT, highly imbalanced sizes of OARs and large inter-slice spacing.
1 code implementation • 27 Jan 2021 • Haochen Mei, Wenhui Lei, Ran Gu, Shan Ye, Zhengwentai Sun, Shichuan Zhang, Guotai Wang
Delineation of Gross Target Volume (GTV) from medical images such as CT and MRI images is a prerequisite for radiotherapy.
2 code implementations • 13 Dec 2020 • Wenhui Lei, Wei Xu, Ran Gu, Hao Fu, Shaoting Zhang, Guotai Wang
To address this problem, we present a one-shot framework for organ and landmark localization in volumetric medical images, which does not need any annotation during the training stage and could be employed to locate any landmarks or organs in test images given a support (reference) image during the inference stage.