ArticleJournal of personalized medicine2024
Statistical Analysis of nnU-Net Models for Lung Nodule Segmentation.
Article in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Automated Lesion Segmentation in Medical Imaging via Integration of nnU-Net Optimization and SAM Approach.Biomedical engineering and computational biology · 2026Article
- Detection and classification of lung cancer using sequential hybridization of CNN and RNN type architectures.Frontiers in big data · 2026Article
- Hybrid intelligence in medical image segmentation.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This paper aims to conduct a statistical analysis of different components of nnU-Net models to build an optimal pipeline for lung nodule segmentation in computed tomography images (CT scan). This study focuses on semantic segmentation of lung nodules, using the UniToChest dataset. Our approach is based on the nnU-Net framework and is designed to configure a whole segmentation pipeline, thereby avoiding many complex design choices, such as data properties and architecture configuration. Although these framework results provide a good starting point, many configurations in this problem can be optimized. In this study, we tested two U-Net-based architectures, using different preprocessing techniques, and we modified the existing hyperparameters provided by nnU-Net. To study the impact of different settings on model segmentation accuracy, we conducted an analysis of variance (ANOVA) statistical analysis. The factors studied included the datasets according to nodule diameter size, model, preprocessing, polynomial learning rate scheduler, and number of epochs. The results of the ANOVA analysis revealed significant differences in the datasets, models, and preprocessing.
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Registered trials
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