Laboratory SSDIA, ENSET University Hassan II Casablanca, Mohammedia, Morocco.
Laboratory SSDIA, ENSET University Hassan II Casablanca, Mohammedia, Morocco.
Comput Med Imaging Graph. 2021 Mar;88:101864. doi: 10.1016/j.compmedimag.2021.101864. Epub 2021 Jan 13.
Cardiac cine magnetic resonance imaging (MRI) continues to be recognized as an established modality for non-invasive assessment of the function and structure of the cardiovascular system. Making full use of fully convolutional neural networks CNNs ability to operate pixel-wise classification, cine MRI sequences can be segmented and volumetric features of three key heart structures are computed for disease prediction. The three key heart structures are the left ventricle cavity, right ventricle cavity and the left ventricle myocardium. In this paper, we suggest an automated pipeline for both cardiac segmentation and diagnosis. The study was conducted on a dataset of 150 patients from Dijon Hospital in the context of the post-2017 Medical Image Computing and Computer Assisted Intervention MICCAI, Automated Cardiac Diagnosis Challenge (ACDC). The challenge consists in two phases: (i) a segmentation contest, where performance is evaluated on dice overlap coefficient and Hausdorff distance metrics, and a (ii) diagnosis contest for heart disease classification. For this aim, we propose the use of a deep learning based network for segmentation of the three key cardiac structures within short-axis cine MRI sequences and a classifier ensemble for heart disease classification. The deep learning segmentation network is a UNet fully convolutional neural network variant with fewer trainable parameters. The classifier ensemble consists in combining three classifiers, namely a multilayer perceptron, a random forest and a support vector machine. Before feeding the segmentation network, a preliminary step consists in localizing heart region and cropping input images to a restricted region of interest (ROI). This is achieved by a signal processing based approach and aims at reducing multi-class imbalance and computational load. We achieved nearly state of the art accuracy performances for both the segmentation and disease classification challenges. Reporting a mean dice overlap coefficient of 0.92 for the three cardiac structures segmentation, along with good limits of agreement for the various derived clinical indices, leading to an accuracy of 0.92 for the disease classification on unseen data.
心脏电影磁共振成像（MRI）继续被认为是一种用于评估心血管系统功能和结构的成熟非侵入性方法。充分利用全卷积神经网络（CNN）对像素进行分类的能力，可以对电影 MRI 序列进行分割，并计算三个关键心脏结构的容积特征，以进行疾病预测。这三个关键心脏结构是左心室腔、右心室腔和左心室心肌。在本文中，我们提出了一种自动的心脏分割和诊断流水线。该研究基于 150 名患者的数据，这些患者来自第戎医院，在 2017 年后的医学图像计算和计算机辅助干预 MICCAI、自动心脏诊断挑战赛（ACDC）中进行。挑战赛分为两个阶段：（i）分割竞赛，根据骰子重叠系数和 Hausdorff 距离度量来评估性能；（ii）心脏疾病分类的诊断竞赛。为此，我们提出了使用基于深度学习的网络来分割短轴电影 MRI 序列中的三个关键心脏结构，并使用分类器集成来进行心脏疾病分类。深度学习分割网络是一种具有较少可训练参数的 UNet 全卷积神经网络变体。分类器集成由三个分类器组成，即多层感知机、随机森林和支持向量机。在将分割网络之前，一个初步步骤是通过信号处理方法来定位心脏区域并裁剪输入图像到一个受限的感兴趣区域（ROI）。这旨在减少多类不平衡和计算负载。我们在分割和疾病分类挑战中都实现了近乎最先进的精度性能。对于三个心脏结构的分割，报告了 0.92 的平均骰子重叠系数，以及各种衍生临床指数的良好一致性界限，从而导致对未见数据的疾病分类的准确率为 0.92。
