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

Malitha Gunawardhana, Fangqiang Xu, Jichao Zhao

TL;DR

This study provides a comprehensive evaluation of nnU-Net for cardiac MRI segmentation across five widely used datasets, examining 2D and multiple 3D configurations as well as ensemble variants. It demonstrates that nnU-Net variants can achieve state-of-the-art or competitive performance across diverse tasks, with 2D models occasionally outperforming 3D and ensembles not always delivering gains due to model diversity and task characteristics. The analysis highlights that segmentation of larger cardiac structures (LA cavity, LV, RV) tends to be robust, while challenging regions like scars and myocardium benefit from task-specific modeling. The findings offer practical guidance on deploying nnU-Net for cardiac MRI and emphasize the ongoing need for specialized models to tackle region-specific segmentation challenges in clinical practice.

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.

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

TL;DR

This study provides a comprehensive evaluation of nnU-Net for cardiac MRI segmentation across five widely used datasets, examining 2D and multiple 3D configurations as well as ensemble variants. It demonstrates that nnU-Net variants can achieve state-of-the-art or competitive performance across diverse tasks, with 2D models occasionally outperforming 3D and ensembles not always delivering gains due to model diversity and task characteristics. The analysis highlights that segmentation of larger cardiac structures (LA cavity, LV, RV) tends to be robust, while challenging regions like scars and myocardium benefit from task-specific modeling. The findings offer practical guidance on deploying nnU-Net for cardiac MRI and emphasize the ongoing need for specialized models to tackle region-specific segmentation challenges in clinical practice.

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.
Paper Structure (48 sections, 5 equations, 9 figures, 12 tables)

This paper contains 48 sections, 5 equations, 9 figures, 12 tables.

Table of Contents

  1. Introduction
  2. Method
  3. nnU-Net architecture
  4. Datasets
  5. LAScarQS Challenge Dataset
  6. 2018 Left Atria Segmentation Challenge (LASC) Dataset
  7. Automated Cardiac Diagnosis Challenge (ACDC) 2017 Dataset
  8. Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Image Segmentation Challenge (MnM-1 and MnM-2)
  9. Implementation Details
  10. Evaluation Metrics
  11. Results
  12. LAScarQS
  13. LASC
  14. ACDC
  15. MnM
  16. ...and 33 more sections

Figures (9)

  • Figure 1: Visualization of the long axis and short axis views in both end diastole and end systole phases for the MnM2 dataset. The right ventricle (RV) is highlighted in white, the Myocardium (MYO) is highlighted in yellow, and the Left Ventricle (LV) is highlighted in red.
  • Figure 2: Comparison of Ground Truth and Predictions from different variations of nnU-Nets for the LAScarQs Task 1. The Left Atrial (LA) cavity is highlighted in green, and LA scars are highlighted in blue. Visualised using Amira 3D software.
  • Figure 3: Comparison of ground truth and predictions from different nnU-Net Versions (2D, 3D Full Resolution, 3D Low Resolution, 3D Cascade, and Ensemble) in three anatomical views: Axial, Sagittal, and Coronal for the LASC dataset. The cavity area is highlighted in Red. Visualized using ITK-SNAP software.
  • Figure 4: Comparison of Ground Truth and Predictions from nnU-Net Variants (2D, 3D Full Resolution, and Ensemble) on the ACDC Dataset for End Systole (ES) and End Diastole (ED) Phases. The right ventricle (RV) is highlighted in red, the myocardium (MYO) is in yellow, and the left ventricle (LV) is in white.
  • Figure 5: Comparison of Ground Truth and Predictions from nnU-Net Variants (2D, 3D Full Resolution, and Ensemble) on the MnM Dataset for End Systole (ES) and End Diastole (ED) Phases. The right ventricle (RV) is highlighted in green, the myocardium (MYO) is in yellow, and the left ventricle (LV) is in red.
  • ...and 4 more figures