arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新，休息一下吧，周末愉快~~
arXiv 2407.04353eess.IVcs.CV

医学图像分割：从UNet到Res-UNet与nnUNet

Segmenting Medical Images: From UNet to Res-UNet and nnUNet

Brunel University London（布鲁内尔大学伦敦分校）

Lina Huang, Alina Miron, Kate Hone, Yongmin Li

备注：
7 pages, 3 figures
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文比较了UNet、Res-UNet、Attention Res-UNet和nnUNet在脑肿瘤、息肉及心脏分割任务中的表现，发现nnUNet整体性能最优，尤其在召回率和准确率上表现突出，适合临床应用。

摘要：

本研究对包括UNet、Res-UNet、Attention Res-UNet和nnUNet在内的深度学习模型进行了比较分析，并评估了它们在脑肿瘤、息肉和多类心脏分割任务中的性能。分析重点关注精确率、准确率、召回率、Dice相似系数（DSC）和交并比（IoU），以评估其临床适用性。在脑肿瘤分割中，Res-UNet和nnUNet显著优于UNet，其中Res-UNet在DSC和IoU得分上领先，表明其在肿瘤勾画方面具有更高的准确性。同时，nnUNet在召回率和准确率方面表现突出，这对于临床诊断和规划中可靠的肿瘤检测至关重要。在息肉检测中，nnUNet最为有效，在所有类别中均取得了最高指标，证明其是内窥镜检查中可靠的诊断工具。在复杂的心脏分割任务中，Res-UNet和Attention Res-UNet在左心室勾画方面表现突出，Res-UNet在右心室分割中也处于领先地位。nnUNet在心肌分割方面无可匹敌，在精确率、召回率、DSC和IoU上均取得了最高分。结论指出，尽管Res-UNet偶尔在特定指标上超过nnUNet，但差异相当小。此外，nnUNet在所有实验中始终表现出更优的整体性能。尤其值得注意的是，nnUNet具有较高的召回率和准确率，这在临床环境中对于减少误诊和确保及时治疗至关重要。nnUNet在所有测试类别的关键指标上表现稳健，使其成为应对这些多样且复杂分割任务的最有效模型。

英文摘要：

This study provides a comparative analysis of deep learning models including UNet, Res-UNet, Attention Res-UNet, and nnUNet, and evaluates their performance in brain tumour, polyp, and multi-class heart segmentation tasks. The analysis focuses on precision, accuracy, recall, Dice Similarity Coefficient (DSC), and Intersection over Union (IoU) to assess their clinical applicability. In brain tumour segmentation, Res-UNet and nnUNet significantly outperformed UNet, with Res-UNet leading in DSC and IoU scores, indicating superior accuracy in tumour delineation. Meanwhile, nnUNet excelled in recall and accuracy, which are crucial for reliable tumour detection in clinical diagnosis and planning. In polyp detection, nnUNet was the most effective, achieving the highest metrics across all categories and proving itself as a reliable diagnostic tool in endoscopy. In the complex task of heart segmentation, Res-UNet and Attention Res-UNet were outstanding in delineating the left ventricle, with Res-UNet also leading in right ventricle segmentation. nnUNet was unmatched in myocardium segmentation, achieving top scores in precision, recall, DSC, and IoU. The conclusion notes that although Res-UNet occasionally outperforms nnUNet in specific metrics, the differences are quite small. Moreover, nnUNet consistently shows superior overall performance across the experiments. Particularly noted for its high recall and accuracy, which are crucial in clinical settings to minimize misdiagnosis and ensure timely treatment, nnUNet's robust performance in crucial metrics across all tested categories establishes it as the most effective model for these varied and complex segmentation tasks.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑