A Comparative Analysis of U-Net-based models for Segmentation of Cardiac MRI

Ketan Suhaas Saichandran
1/18/2024
eess.IVcs.CVcs.LG

No confident code implementation found for this paper. 3 possible matches can be shown below.

Abstract

Medical imaging refers to the technologies and methods utilized to view the human body and its inside, in order to diagnose, monitor, or even treat medical disorders. This paper aims to explore the application of deep learning techniques in the semantic segmentation of Cardiac short-axis MRI (Magnetic Resonance Imaging) images, aiming to enhance the diagnosis, monitoring, and treatment of medical disorders related to the heart. The focus centers on implementing various architectures that are derivatives of U-Net, to effectively isolate specific parts of the heart for comprehensive anatomical and functional analysis. Through a combination of images, graphs, and quantitative metrics, the efficacy of the models and their predictions are showcased. Additionally, this paper addresses encountered challenges and outline strategies for future improvements. This abstract provides a concise overview of the efforts in utilizing deep learning for cardiac image segmentation, emphasizing both the accomplishments and areas for further refinement.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

A Novel Deep Learning Method for Segmenting the Left Ventricle in Cardiac Cine MRI

90% match2026

Multi-Stage Segmentation and Cascade Classification Methods for Improving Cardiac MRI Analysis

89% match2024

Cardiac MRI Semantic Segmentation for Ventricles and Myocardium using Deep Learning

89% match2025

Two Deep Learning Approaches for Automated Segmentation of Left Ventricle in Cine Cardiac MRI

88% match2026

SFB-net for cardiac segmentation: Bridging the semantic gap with attention

87% match2024

How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation

87% match2024

Cite this paper

@article{saichandran2024comparative,
  title  = {A Comparative Analysis of U-Net-based models for Segmentation of Cardiac MRI},
  author = {Ketan Suhaas Saichandran},
  year   = {2024},
  eprint = {2401.09980},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.09980v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2401.09980.svg)](https://codeofpaper.com/paper/2401.09980)

Discussion