超越像素:通过传统机器学习和图卷积网络进行基于超像素的磁共振成像分割

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer methods and programs in biomedicine Pub Date : 2024-08-28 DOI:10.1016/j.cmpb.2024.108398
Zakia Khatun , Halldór Jónsson Jr. , Mariella Tsirilaki , Nicola Maffulli , Francesco Oliva , Pauline Daval , Francesco Tortorella , Paolo Gargiulo
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引用次数: 0

摘要

背景和目的:肌腱分割对于研究肌腱相关病症(如肌腱病、肌腱变性等)至关重要。通过这一步骤,可以使用自动化或半自动化方法进一步对特定肌腱区域进行详细分析。方法:本研究提出了一个全面的端到端肌腱分割模块,由基于超像素的初步粗分割和最终分割任务组成。最终的分割结果通过两种不同的方法获得。在第一种方法中,使用随机森林(RF)和支持向量机(SVM)分类器对粗略生成的超像素进行分类,以确定每个超像素是否属于肌腱类别(从而进行肌腱分割)。在第二种方法中,超像素的排列被转换成图,而不是传统的图像网格。这一分类过程使用基于图的卷积网络(GCN)来确定每个超像素是否对应于肌腱类别。数据集由 76 名受试者组成,分为两组:一组用于训练(数据集 1,采用 "留一弃组 "交叉验证法进行训练和评估),另一组作为未见测试数据(数据集 2)。使用我们的第一种方法,RF 和 SVM 分类器在测试数据(数据集 2)上的最终测试 AUC(ROC 曲线下面积)得分分别为 0.992 和 0.987,灵敏度分别为 0.904 和 0.966。另一方面,使用我们的第二种方法(基于 GCN 的节点分类),测试集的 AUC 得分为 0.933,灵敏度为 0.899。无论是利用射频、基于 SVM 的超像素分类,还是基于 GCN 的分类进行肌腱分割,我们的系统都能获得值得称赞的 AUC 分数,尤其是非基于图谱的方法。由于数据集有限,我们基于图的方法的表现不如非基于图的超像素分类方法;但是,所获得的结果为我们了解模型如何区分肌腱和非肌腱提供了宝贵的见解。这为进一步探索和改进提供了机会。
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Beyond pixel: Superpixel-based MRI segmentation through traditional machine learning and graph convolutional network

Background and Objective:

Tendon segmentation is crucial for studying tendon-related pathologies like tendinopathy, tendinosis, etc. This step further enables detailed analysis of specific tendon regions using automated or semi-automated methods. This study specifically aims at the segmentation of Achilles tendon, the largest tendon in the human body.

Methods:

This study proposes a comprehensive end-to-end tendon segmentation module composed of a preliminary superpixel-based coarse segmentation preceding the final segmentation task. The final segmentation results are obtained through two distinct approaches. In the first approach, the coarsely generated superpixels are subjected to classification using Random Forest (RF) and Support Vector Machine (SVM) classifiers to classify whether each superpixel belongs to a tendon class or not (resulting in tendon segmentation). In the second approach, the arrangements of superpixels are converted to graphs instead of being treated as conventional image grids. This classification process uses a graph-based convolutional network (GCN) to determine whether each superpixel corresponds to a tendon class or not.

Results:

All experiments are conducted on a custom-made ankle MRI dataset. The dataset comprises 76 subjects and is divided into two sets: one for training (Dataset 1, trained and evaluated using leave-one-group-out cross-validation) and the other as unseen test data (Dataset 2). Using our first approach, the final test AUC (Area Under the ROC Curve) scores using RF and SVM classifiers on the test data (Dataset 2) are 0.992 and 0.987, respectively, with sensitivities of 0.904 and 0.966. On the other hand, using our second approach (GCN-based node classification), the AUC score for the test set is 0.933 with a sensitivity of 0.899.

Conclusions:

Our proposed pipeline demonstrates the efficacy of employing superpixel generation as a coarse segmentation technique for the final tendon segmentation. Whether utilizing RF, SVM-based superpixel classification, or GCN-based classification for tendon segmentation, our system consistently achieves commendable AUC scores, especially the non-graph-based approach. Given the limited dataset, our graph-based method did not perform as well as non-graph-based superpixel classifications; however, the results obtained provide valuable insights into how well the models can distinguish between tendons and non-tendons. This opens up opportunities for further exploration and improvement.

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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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