Yu Qi, Ning Hongxia, Yang Jinzhu, Li Chen, Qi Yiqiu, Qu Mingjun, Li Honghe, Sun Song, Cao Peng, Feng Chaolu
Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China.
Department of Cardiovascular Ultrasound, The First Hospital of China Medical University, Shenyang, China; Clinical Medical Research Center of Imaging in Liaoning Province, Shenyang, China.
Comput Methods Programs Biomed. 2025 Mar;260:108544. doi: 10.1016/j.cmpb.2024.108544. Epub 2024 Dec 13.
Left ventricular myocardium segmentation is of great significance for clinical diagnosis, treatment, and prognosis. However, myocardium segmentation is challenging as the medical image quality is disturbed by various factors such as motion, artifacts, and noise. Its accuracy largely depends on the accurate identification of edges and structures. Most existing encoder-decoder based segmentation methods capture limited contextual information and ignore the awareness of myocardial shape and structure, often producing unsatisfactory boundary segmentation results in noisy scenes. Moreover, these methods fail to assess the reliability of the predictions, which is crucial for clinical decisions and applications in medical tasks. Therefore, this study explores how to effectively combine contextual information with myocardial edge structure and confidence maps to improve segmentation performance in an end-to-end network.
In this paper, we propose an end-to-end confidence map refinement boundary enhancement network (CMR-BENet) for left ventricular myocardium segmentation. CMR-BENet has three components: a layer semantic-aware module (LSA), an edge information enhancement module (EIE), and a confidence map-based refinement module (CMR). Specifically, LSA first adaptively fuses high- and low-level semantic information across hierarchical layers to mitigate the bias of single-layer features affected by noise. EIE then improves the edge and structure recognition by designing the edge and mask guidance module (EMG) and the edge structure-aware module (ESA). Finally, CMR provides a simple and efficient way to estimate confidence maps and effectively combines the encoder features to refine the segmentation results.
Experiments on two echocardiography datasets and one cardiac MRI dataset show that the proposed CMR-BENet outperforms its rivals in the left ventricular myocardium segmentation task with Dice (DI) of 87.71%, 79.33%, and 89.11%, respectively.
This paper utilizes edge information to characterize the shape and structure of the myocardium and introduces learnable confidence maps to evaluate and refine the segmentation results. Our findings provide strong support and reference for physicians in diagnosis and treatment.
左心室心肌分割对于临床诊断、治疗及预后具有重要意义。然而，由于医学图像质量受到运动、伪影和噪声等多种因素干扰，心肌分割颇具挑战性。其准确性很大程度上取决于边缘和结构的准确识别。大多数现有的基于编码器 - 解码器的分割方法捕获的上下文信息有限，且忽略了对心肌形状和结构的感知，在噪声场景中往往产生不尽人意的边界分割结果。此外，这些方法无法评估预测的可靠性，而这对于医疗任务中的临床决策和应用至关重要。因此，本研究探索如何在端到端网络中有效地将上下文信息与心肌边缘结构和置信度图相结合，以提高分割性能。
本文提出一种用于左心室心肌分割的端到端置信度图细化边界增强网络（CMR - BENet）。CMR - BENet由三个部分组成：层语义感知模块（LSA）、边缘信息增强模块（EIE）和基于置信度图的细化模块（CMR）。具体而言，LSA首先跨层次自适应融合高低层语义信息，以减轻受噪声影响的单层特征偏差。然后，EIE通过设计边缘和掩码引导模块（EMG）以及边缘结构感知模块（ESA）来提高边缘和结构识别能力。最后，CMR提供一种简单有效的方法来估计置信度图，并有效结合编码器特征以细化分割结果。
在两个超声心动图数据集和一个心脏磁共振成像数据集上的实验表明，所提出的CMR - BENet在左心室心肌分割任务中优于其竞争对手，Dice系数分别为87.71%、79.33%和89.11%。
本文利用边缘信息表征心肌的形状和结构，并引入可学习的置信度图来评估和细化分割结果。我们的研究结果为医生的诊断和治疗提供了有力支持和参考。
