Convolutional module for heart localization and segmentation in MRI

Magnetic resonance imaging (MRI) is a medical imaging technique used to capture volumetric image sequences of internal soft tissues, such as cardiac muscles. In comparison to X-Ray imaging (XR) and Computer Tomography (CT), MRI provides images with improved structural details via finer spatial resolutions. Cardiac MRI (CMR) focuses on the heart, allowing trained cardiologists to measure heart parameters, for example the mass of the cardiac muscle (myocardium mass), the volumes of blood cavities (atrial and ventricular volumes) and the amount of blood pumped per heartbeat (ejection fraction) [Peng et al., 2016]. Those parameters are used to assess how healthy is the heart, by recognizing early conditions and signs before the onset of infarcts and other complications. Due to the size and complexity of CMR sequences, complex techniques are required to produce detailed analyses; one of these techniques is deep learning (DL). Many of the tasks and goals related to the cardiac functional analysis - for example segmentation of structures [Bernard et al., 2018], estimation of heart parameters [Xue et al., 2018], and detection of diseases [Khened et al., 2017] - have benefited from DL methods. For even better results, research in DL has pointed out that models based on convolutional neural networks (CNN) have had a higher efficacy when provided with regions-of-interest (ROI) either explicitly or implicitly [Xue et al., 2018]. The detection of ROIs, usually named ROI proposal, is a preprocessing step whose goal is to identify the most prominent regions of an image (frame) for discovering clinically-relevant artifacts. The explicit ROI proposal approaches usually follow a combination of methods, for example: (a) pipelining a segmentation and a regression network; (b) preprocessing the input with a region proposal algorithm [He et al., 2015] or with a CNN [Wu et al., 2020]; or (c) by using manual cropping [Xue et al., 2017].