由反事实生成学习驱动的早产儿点状白质病变分割
Punctate White Matter Lesion Segmentation in Preterm Infants Powered by Counterfactually Generative Learning
AI总结：
针对早产儿脑部MR图像中点状白质病变（PWMLs）分割困难的问题，提出基于反事实推理与脑组织分割辅助任务的DeepPWML框架，通过结合病变反事实图与组织概率图训练轻量级网络，实现了最先进的准确分割性能。
摘要：
点状白质病变（PWMLs）的准确分割是及时诊断和治疗相关发育障碍的基础。考虑到病变通常很小且对比度低，且病变数量在不同受试者之间可能发生剧烈变化，从婴儿脑部MR图像中自动进行PWMLs分割具有挑战性。现有的基于学习的方法直接将通用网络架构应用于这一具有挑战性的任务，可能无法捕获PWMLs的详细位置信息，从而导致严重的分割不足。在本文中，我们提出利用反事实推理的思想，结合脑组织分割的辅助任务，来学习PWMLs的细粒度位置和形态表示，以实现准确的定位和分割。相应地，设计了一个简单且易于实现的深度学习框架（即DeepPWML）。它将病变反事实图与组织概率图相结合，以训练轻量级的PWML分割网络，在婴儿T1w MR图像的真实临床数据集上展示了最先进的性能。代码可在https://github.com/ladderlab-xjtu/DeepPWML获取。
英文摘要：
Accurate segmentation of punctate white matter lesions (PWMLs) are fundamental for the timely diagnosis and treatment of related developmental disorders. Automated PWMLs segmentation from infant brain MR images is challenging, considering that the lesions are typically small and low-contrast, and the number of lesions may dramatically change across subjects. Existing learning-based methods directly apply general network architectures to this challenging task, which may fail to capture detailed positional information of PWMLs, potentially leading to severe under-segmentations. In this paper, we propose to leverage the idea of counterfactual reasoning coupled with the auxiliary task of brain tissue segmentation to learn fine-grained positional and morphological representations of PWMLs for accurate localization and segmentation. A simple and easy-to-implement deep-learning framework (i.e., DeepPWML) is accordingly designed. It combines the lesion counterfactual map with the tissue probability map to train a lightweight PWML segmentation network, demonstrating state-of-the-art performance on a real-clinical dataset of infant T1w MR images. The code is available at \href{https://github.com/ladderlab-xjtu/DeepPWML}{https://github.com/ladderlab-xjtu/DeepPWML}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
