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.

Abderazzak Ammar

Omar Bouattane

Mohamed Youssfi

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

《计算机医学成像与图形》杂志的目的是作为交流研究成果的来源，这些研究结果涉及算法进步、开发和数字成像在疾病检测、诊断、干预、预防、精准医学和人口健康方面的应用。该杂志将收录有关新型计算机成像或可视化技术的文章，包括人工智能和机器学习、用于手术计划和指导的增强现实、大生物医学数据可视化、计算机辅助诊断、计算机机器人手术、图像引导治疗、成像扫描和重建、移动和远程成像、放射组学以及与数字健康相关的其他信息的成像集成和建模。生物医学成像的类型包括：磁共振、计算机断层扫描、超声、核医学、X 射线、微波、光学和多光子显微镜、视频和感官成像，以及生物医学图像与其他非成像数据集的融合。

The journal "Computerized Medical Imaging and Graphics" aims to serve as a platform for the exchange of research findings related to advancements in algorithms, development, and the application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. It publishes articles on novel computer imaging or visualization technologies, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, large biomedical data visualization, computer-aided diagnosis, robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and remote imaging, radiomics, and other imaging integration and modeling related to digital health. Types of biomedical imaging covered include magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-rays, microwaves, optical and multiphoton microscopy, video and sensory imaging, as well as the fusion of biomedical images with other non-imaging datasets.

《Computerized Medical Imaging and Graphics》杂志旨在交流研究成果，涵盖算法进步、开发及数字成像在疾病检测、诊断、干预、预防、精准医学和人口健康中的应用。该杂志将收录关于新型计算机成像或可视化技术的文章，包括人工智能和机器学习、用于手术计划和指导的增强现实、大生物医学数据可视化、计算机辅助诊断、计算机机器人手术、图像引导治疗、成像扫描与重建、移动和远程成像、放射组学，以及与数字健康相关的其他成像集成和建模信息。生物医学成像的类型包括：磁共振、计算机断层扫描、超声、核医学、X射线、微波、光学和多光子显微镜、视频和感官成像，以及生物医学图像与其他非成像数据集的融合。

Computerized Medical Imaging and Graphics

Comput Med Imaging Graph

Automatic cardiac cine MRI segmentation and heart disease classification.

A deep learning based network is proposed for segmentation of the three key cardiac structures within short-axis cine MRI sequences and a classifier ensemble for heart disease classification with nearly state of the art accuracy performances for both the segmentation and disease classification challenges.

Category	Quartile
医学	2
医学, 工程生物医学	2
医学, 核医学	2

Automatic cardiac cine MRI segmentation and heart disease classification.

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Journal Info

Category	Quartile
RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING	1