Jerónimo Alejandro, Valenzuela Olga, Rojas Ignacio
Computer Engineering, Automatics and Robotics Department, University of Granada, 18071 Granada, Spain.
Department of Applied Mathematics, University of Granada, 18071 Granada, Spain.
J Pers Med. 2024 Sep 24;14(10):1016. doi: 10.3390/jpm14101016.
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.
本文旨在对nnU-Net模型的不同组件进行统计分析，以构建用于计算机断层扫描图像（CT扫描）中肺结节分割的最优流程。本研究聚焦于使用UniToChest数据集进行肺结节的语义分割。我们的方法基于nnU-Net框架，旨在配置一个完整的分割流程，从而避免许多复杂的设计选择，如数据属性和架构配置。尽管这些框架结果提供了一个良好的起点，但该问题中的许多配置仍可优化。在本研究中，我们测试了两种基于U-Net的架构，使用不同的预处理技术，并修改了nnU-Net提供的现有超参数。为了研究不同设置对模型分割准确性的影响，我们进行了方差分析（ANOVA）统计分析。所研究的因素包括根据结节直径大小划分的数据集、模型、预处理、多项式学习率调度器和训练轮数。方差分析的结果显示，数据集、模型和预处理方面存在显著差异。
