Abstract
This research aims to develop a novel deep learning network, GBU-Net, utilizing a group-batch-normalized U-Net framework, specifically designed for the precise semantic segmentation of the left ventricle in short-axis cine MRI scans. The methodology includes a down-sampling pathway for feature extraction and an up-sampling pathway for detail restoration, enhanced for medical imaging. Key modifications include techniques for better contextual understanding crucial in cardiac MRI segmentation. The dataset consists of 805 left ventricular MRI scans from 45 patients, with comparative analysis using established metrics such as the dice coefficient and mean perpendicular distance. GBU-Net significantly improves the accuracy of left ventricle segmentation in cine MRI scans. Its innovative design outperforms existing methods in tests, surpassing standard metrics like the dice coefficient and mean perpendicular distance. The approach is unique in its ability to capture contextual information, often missed in traditional CNN-based segmentation. An ensemble of the GBU-Net attains a 97% dice score on the SunnyBrook testing dataset. GBU-Net offers enhanced precision and contextual understanding in left ventricle segmentation for surgical robotics and medical analysis.
Abstract (translated)
这项研究旨在开发一种新型深度学习网络GBU-Net，利用组批归一化（group-batch-normalized）的U型网框架，专门用于短轴心脏电影磁共振成像（cine MRI）扫描中左心室的精确语义分割。该方法包括一个下采样路径进行特征提取和一个上采样路径恢复细节，并针对医学影像进行了增强。关键改进在于采用了有助于改善心血管MRI分割上下文理解的技术。 数据集由来自45名患者的805个左心室MRI扫描图像组成，通过已建立的指标如骰子系数（dice coefficient）和平均垂直距离（mean perpendicular distance）进行比较分析。GBU-Net显著提高了心脏电影MRI中左心室分割的准确性。其创新设计在测试中优于现有方法，在骰子系数和平均垂直距离等标准度量上表现更佳。 该方法的独特之处在于它能够捕捉到上下文信息，这是传统基于CNN（卷积神经网络）的方法常常忽略的重要细节。GBU-Net的集成模型在SunnyBrook测试数据集上达到了97%的骰子分数。GBU-Net为心脏手术机器人和医学分析提供了更精确、更具上下文理解能力的左心室分割方法。
URL
https://arxiv.org/abs/2601.01512
