Full Motion Focus: Convolutional Module for Improved Left Ventricle Segmentation over 4D MRI Daniel M. Lima ⋆1,2[0000-0002-7818-6103] , Catharine V. Graves 2[0000-0001-7274-2044] , Marco A. Gutierrez 2[0000-0003-0964-6222] , Bruno Brandoli 3[0000-0001-6167-8104] , and Jose F. Rodrigues Jr. 1[0000-0001-8318-1780] 1 Institute of Mathematics and Computer Science, Universidade de Sao Paulo, Sao Carlos SP, Brazil 2 Laboratorio de Informatica Biomedica, Instituto do Coracao, Hospital das Clinicas, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo SP, Brazil 3 Dalhousie University, B3H 4R2 Halifax NS, Canada Abstract. Magnetic Resonance Imaging (MRI) is a widely known medical imaging technique used to assess the heart function. Over Cardiac MRI (CMR) images, Deep Learning (DL) models perform several tasks with good efficacy, such as segmentation, estimation, and detection of diseases. Such models can produce even better results when their input is a Region of Interest (RoI), that is, a segment of the image with more analytical potential for diagnosis. Accordingly, we describe Full Motion Focus (FMF ), an image processing technique sensitive to the heart motion in a 4D MRI sequence (video) whose principle is to combine static and dynamic image features with a Radial Basis Function (RBF) to highlight the RoI found in the motion field. We experimented FMF with the U-Net convolutional DL architecture over three CMR datasets in the task of Left Ventricle segmentation; we achieved a rate of detection (Recall score) of 99.7% concerning the RoIs, improved the U-Net segmentation (mean Dice score) by 1.7 (p<.001), and improved the overall training speed by 2.5 times (+150%). Keywords: Cardiac MRI · Motion · Deep learning · Localization · Segmen- tation 1 Introduction Magnetic resonance imaging (MRI) is a medical imaging technique used to capture vol- umetric image sequences of internal soft tissues, such as cardiac muscles. In comparison to X-ray imaging (XR) and computed 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), volumes of blood cavities (atrial and ventricular volumes) and volume of blood pumped per heartbeat (ejection frac- tion) [9]. 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. ⋆ Corresponding author: D. M. Lima, danielm@usp.br