标题： 使用最少标注的3D nnU-Net进行小鼠MRI扫描中的肺肿瘤分割 显示英文标题

标题： Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations

摘要： 在药物发现过程中，准确的肺肿瘤分割是使用诸如MRI等\textit{体内}成像评估肿瘤大小及其进展的重要步骤。虽然已经开发出了深度学习模型来自动化这一过程，但重点主要集中在人类受试者上，忽略了动物模型在临床前药物开发中的关键作用。在这项工作中，我们专注于优化小鼠的肺肿瘤分割。首先，我们证明了nnU-Net模型的表现优于U-Net、U-Net3+和DeepMeta模型。最重要的是，我们使用nnU-Net 3D模型取得了比2D模型更好的结果，这表明空间上下文对于MRI小鼠扫描中的分割任务的重要性。本研究展示了在MRI扫描中肺肿瘤分割中3D输入相对于2D输入图像的重要性。最后，我们在肺和肺内肿瘤的联合分割方面超过了先前最先进的方法。我们的工作仅使用肺肿瘤标注即可实现可比的结果，所需的标注更少，节省了时间和标注努力。这项工作（https://anonymous.4open.science/r/lung-tumour-mice-mri-64BB）是自动化临床前动物研究以量化实验药物疗效的重要一步，特别是在评估肿瘤变化方面。 显示英文摘要

摘要： In drug discovery, accurate lung tumor segmentation is an important step for assessing tumor size and its progression using \textit{in-vivo} imaging such as MRI. While deep learning models have been developed to automate this process, the focus has predominantly been on human subjects, neglecting the pivotal role of animal models in pre-clinical drug development. In this work, we focus on optimizing lung tumor segmentation in mice. First, we demonstrate that the nnU-Net model outperforms the U-Net, U-Net3+, and DeepMeta models. Most importantly, we achieve better results with nnU-Net 3D models than 2D models, indicating the importance of spatial context for segmentation tasks in MRI mice scans. This study demonstrates the importance of 3D input over 2D input images for lung tumor segmentation in MRI scans. Finally, we outperform the prior state-of-the-art approach that involves the combined segmentation of lungs and tumors within the lungs. Our work achieves comparable results using only lung tumor annotations requiring fewer annotations, saving time and annotation efforts. This work (https://anonymous.4open.science/r/lung-tumour-mice-mri-64BB) is an important step in automating pre-clinical animal studies to quantify the efficacy of experimental drugs, particularly in assessing tumor changes.