基于自监督变分自编码器的医学图像分割

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yanjie Zhou , Feng Zhou , Fengjun Xi , Yong Liu , Yun Peng , David E. Carlson , Liyun Tu
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引用次数: 0

摘要

少镜头医学图像分割通常使用联合模型进行配准和分割。配准模型将已标记的地图集与未标记的图像对齐形成初始掩码,然后由分割模型对其进行细化。然而,在配准过程中不可避免的空间错位会导致不准确和分割质量下降。为了解决这个问题,我们开发了EFS-MedSeg,这是一个端到端模型,使用两个标记的地图集和少量未标记的图像,通过数据增强和自监督学习进行增强。最初,EFS-MedSeg采用3D随机区域切换策略来增加地图集,从而增强对分割任务的监督。这不仅为训练数据引入了可变性,而且增强了模型的泛化能力和防止过拟合的能力,从而获得自然平滑的标签边界。在此之后,我们使用变分自编码器进行加权重建任务,将模型的注意力集中在Dice得分较低的区域,以确保符合地图集图像形状和结构外观的准确分割。此外,我们引入了一个自对比模块,旨在改进特征提取,以解剖结构先验为指导,从而提高模型的收敛性和分割精度。在多模态医学图像数据集上的结果表明,EFS-MedSeg达到了与全监督方法相当的性能。此外,在OASIS、BCV和BCH数据集上,该方法在Dice得分方面一直比第二好的方法分别高出1.4%、9.1%和1.1%,突出了其在不同数据集上的鲁棒性和适应性。源代码将在https://github.com/NoviceFodder/EFS-MedSeg上公开提供。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient few-shot medical image segmentation via self-supervised variational autoencoder
Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with unlabeled images to form initial masks, which are then refined by the segmentation model. However, inevitable spatial misalignments during registration can lead to inaccuracies and diminished segmentation quality. To address this, we developed EFS-MedSeg, an end-to-end model using two labeled atlases and few unlabeled images, enhanced by data augmentation and self-supervised learning. Initially, EFS-MedSeg applies a 3D random regional switch strategy to augment atlases, thereby enhancing supervision in segmentation tasks. This not only introduces variability to the training data but also enhances the model’s ability to generalize and prevents overfitting, resulting in natural and smooth label boundaries. Following this, we use a variational autoencoder for a weighted reconstruction task, focusing the model’s attention on areas with lower Dice scores to ensure accurate segmentation that conforms to the atlas image’s shape and structural appearance. Moreover, we introduce a self-contrastive module aimed at improving feature extraction, guided by anatomical structure priors, thus enhancing the model’s convergence and segmentation accuracy. Results on multi-modal medical image datasets show that EFS-MedSeg achieves performance comparable to fully-supervised methods. Moreover, it consistently surpasses the second-best method in Dice score by 1.4%, 9.1%, and 1.1% on the OASIS, BCV, and BCH datasets, respectively, highlighting its robustness and adaptability across diverse datasets. The source code will be made publicly available at: https://github.com/NoviceFodder/EFS-MedSeg.
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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