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Statistical Analysis of nnU-Net Models for Lung Nodule Segmentation
Alejandro Jerónimo1, Olga Valenzuela2, Ignacio Rojas1
1Computer Engineering, Automatics and Robotics Department, University of Granada, 18071 Granada, Spain.
Journal of Personalized Medicine
|October 25, 2024
Summary
This study optimizes nnU-Net pipelines for lung nodule segmentation in CT scans. Statistical analysis revealed significant impacts of dataset, model, and preprocessing on segmentation accuracy.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Radiology
Background:
- Accurate lung nodule segmentation in CT scans is crucial for early disease detection.
- The nnU-Net framework offers a robust baseline but requires optimization for specific tasks like lung nodule segmentation.
Purpose of the Study:
- To statistically analyze and optimize components of the nnU-Net framework for lung nodule segmentation.
- To identify key factors influencing segmentation performance using the UniToChest dataset.
Main Methods:
- Utilized the nnU-Net framework with two U-Net architectures.
- Investigated various preprocessing techniques and modified hyperparameters.
- Performed Analysis of Variance (ANOVA) to assess the impact of factors like nodule size, model, preprocessing, learning rate, and epochs.
Main Results:
- ANOVA revealed significant differences in segmentation accuracy based on the dataset, chosen model, and preprocessing methods.
- Identified specific configurations that impact the performance of lung nodule segmentation.
Conclusions:
- The choice of dataset, nnU-Net model architecture, and preprocessing significantly affects lung nodule segmentation accuracy.
- Further optimization of these components is essential for building high-performing lung nodule segmentation pipelines.
