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

TL;DR

This paper comprehensively evaluates nnU-Net's performance in cardiac MRI segmentation across multiple datasets and configurations, highlighting its robustness and identifying areas for potential model improvements.

Contribution

The study systematically benchmarks nnU-Net configurations for cardiac MRI segmentation, providing insights into its effectiveness and the need for specialized models.

Findings

nnU-Net achieves high accuracy across datasets

Different configurations offer trade-offs in performance and efficiency

Some tasks may require specialized models beyond nnU-Net

Abstract

Cardiac segmentation is a critical task in medical imaging, essential for detailed analysis of heart structures, which is crucial for diagnosing and treating various cardiovascular diseases. With the advent of deep learning, automated segmentation techniques have demonstrated remarkable progress, achieving high accuracy and efficiency compared to traditional manual methods. Among these techniques, the nnU-Net framework stands out as a robust and versatile tool for medical image segmentation. In this study, we evaluate the performance of nnU-Net in segmenting cardiac magnetic resonance images (MRIs). Utilizing five cardiac segmentation datasets, we employ various nnU-Net configurations, including 2D, 3D full resolution, 3D low resolution, 3D cascade, and ensemble models. Our study benchmarks the capabilities of these configurations and examines the necessity of developing new models for specific cardiac segmentation tasks.