标题： 基于多参数MRI的视觉通路描绘的跨序列半监督学习 显示英文标题

标题： Cross-Sequence Semi-Supervised Learning for Multi-Parametric MRI-Based Visual Pathway Delineation

摘要： 准确描绘视觉通路（VP）对于理解人类视觉系统和诊断相关疾病至关重要。探索多参数磁共振成像（MR）数据已被确定为描绘VP的重要方法。然而，由于复杂的跨序列关系，现有的方法无法有效建模来自不同MRI序列的互补信息。此外，这些现有方法严重依赖于带有标签的大规模训练数据，而获取这些数据既费时又耗力。在这项工作中，我们提出了一种新颖的半监督多参数特征分解框架用于VP描绘。具体而言，设计了一种相关性约束的特征分解（CFD），通过捕获每个MRI序列的独特特性来处理复杂的跨序列关系，并简化多参数信息融合过程。此外，开发了一种基于一致性的样本增强（CSE）模块，通过从无标签数据生成并促进有意义的边缘信息，以解决标注数据有限的问题。我们在两个公开数据集以及一个内部的多壳扩散磁共振成像（MDM）数据集上验证了我们的框架。实验结果表明，与七种最先进的方法相比，我们的方法在描绘性能方面具有明显优势。 显示英文摘要

摘要： Accurately delineating the visual pathway (VP) is crucial for understanding the human visual system and diagnosing related disorders. Exploring multi-parametric MR imaging data has been identified as an important way to delineate VP. However, due to the complex cross-sequence relationships, existing methods cannot effectively model the complementary information from different MRI sequences. In addition, these existing methods heavily rely on large training data with labels, which is labor-intensive and time-consuming to obtain. In this work, we propose a novel semi-supervised multi-parametric feature decomposition framework for VP delineation. Specifically, a correlation-constrained feature decomposition (CFD) is designed to handle the complex cross-sequence relationships by capturing the unique characteristics of each MRI sequence and easing the multi-parametric information fusion process. Furthermore, a consistency-based sample enhancement (CSE) module is developed to address the limited labeled data issue, by generating and promoting meaningful edge information from unlabeled data. We validate our framework using two public datasets, and one in-house Multi-Shell Diffusion MRI (MDM) dataset. Experimental results demonstrate the superiority of our approach in terms of delineation performance when compared to seven state-of-the-art approaches.