使用最小标注的3D nnU-Net在小鼠MRI扫描中进行肺部肿瘤分割
Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations
AI总结：
本研究针对小鼠MRI肺部肿瘤分割，证明3D nnU-Net优于2D模型及现有方法，仅用肿瘤标注即可达到可比效果，减少标注成本，助力临床前药物评估。
摘要：
在药物发现中，准确的肺部肿瘤分割是评估肿瘤大小及其进展的重要步骤，通常使用MRI等体内成像技术。尽管已经开发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，忽视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部肿瘤分割。首先，我们证明了nnU-Net模型优于U-Net、U-Net3+和DeepMeta模型。最重要的是，我们使用nnU-Net 3D模型获得了比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。这项研究证明了在MRI扫描中，3D输入比2D输入图像对肺部肿瘤分割更重要。最后，我们超越了先前最先进的方法，该方法涉及肺部和肺部肿瘤的联合分割。我们的工作仅使用肺部肿瘤标注就取得了可比较的结果，需要的标注更少，节省了时间和标注工作。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。
英文摘要：
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.
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