# Convolutional module for heart localization and segmentation in MRI

arXiv: https://arxiv.org/abs/2107.09134 · HTML: https://mlsanity.com/paper/2107.09134/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement](https://mlsanity.com/paper/1810.08768/) (2018)
- [FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation](https://mlsanity.com/paper/2003.04492/) (2020)
- [Left Ventricle Segmentation via Optical-Flow-Net from Short-axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion](https://mlsanity.com/paper/1810.08753/) (2018)
- [Full Quantification of Left Ventricle via Deep Multitask Learning Network Respecting Intra- and Inter-Task Relatedness](https://mlsanity.com/paper/1706.01912/) (2017)

## Abstract (arXiv)

Magnetic resonance imaging (MRI) is a widely known medical imaging technique used to assess the heart function. Deep learning (DL) models perform several tasks in cardiac MRI (CMR) images with good efficacy, such as segmentation, estimation, and detection of diseases. Many DL models based on convolutional neural networks (CNN) were improved by detecting regions-of-interest (ROI) either automatically or by hand. In this paper we describe Visual-Motion-Focus (VMF), a module that detects the heart motion in the 4D MRI sequence, and highlights ROIs by focusing a Radial Basis Function (RBF) on the estimated motion field. We experimented and evaluated VMF on three CMR datasets, observing that the proposed ROIs cover 99.7% of data labels (Recall score), improved the CNN segmentation (mean Dice score) by 1.7 (p < .001) after the ROI extraction, and improved the overall training speed by 2.5 times (+150%).