使用MR图像进行脑肿瘤分割：将边缘整合进带有可解释激活图的U-Net模型
Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images
AI总结：
本文提出将肿瘤边缘信息整合进U-Net及其变体模型中，利用类导数滤波器提取边缘真实标签进行联合训练，在BraTS2020数据集上提升了脑肿瘤分割精度，并生成可解释的边缘图与激活图辅助治疗规划。
摘要：
从磁共振（MR）图像中手动勾画肿瘤区域非常耗时，需要专家参与，且容易出现人为误差。近年来，深度学习模型已成为脑肿瘤分割的首选方法。U-Net及其变体在医学图像语义分割方面已在文献中取得了良好的结果。然而，U-Net及其变体倾向于过度分割肿瘤区域，且可能无法准确分割肿瘤边缘。肿瘤边缘与肿瘤区域同样重要，对于准确诊断、手术精度和治疗规划至关重要。在提出的工作中，作者旨在使用类导数滤波器从真实标签中提取边缘，随后进行边缘重建，以获得脑肿瘤真实标签之外的边缘真实标签。利用这两种真实标签，作者研究了多种U-Net及其变体架构，对比了是否将肿瘤边缘真实标签作为目标与肿瘤真实标签一同用于脑肿瘤分割。作者使用BraTS2020基准数据集进行研究，并记录了dice和Hausdorff95指标的结果。计算了整个肿瘤（WT）、肿瘤核心（TC）和增强肿瘤（ET）区域的平均值和中位数指标。与基线U-Net及其变体相比，在肿瘤区域之外同时学习边缘的模型在训练和验证数据集的核心肿瘤区域中表现更好。在U-Net和V-Net等基线模型上训练的边缘模型取得了与Swin U-Net和混合MR-U-Net等基线最先进模型相似的性能。边缘目标训练的模型能够生成可用于治疗规划的边缘图。此外，为了进一步解释结果，研究了混合MR-U-Net生成的激活图。
英文摘要：
Manual delineation of tumor regions from magnetic resonance (MR) images is time-consuming, requires an expert, and is prone to human error. In recent years, deep learning models have been the go-to approach for the segmentation of brain tumors. U-Net and its' variants for semantic segmentation of medical images have achieved good results in the literature. However, U-Net and its' variants tend to over-segment tumor regions and may not accurately segment the tumor edges. The edges of the tumor are as important as the tumor regions for accurate diagnosis, surgical precision, and treatment planning. In the proposed work, the authors aim to extract edges from the ground truth using a derivative-like filter followed by edge reconstruction to obtain an edge ground truth in addition to the brain tumor ground truth. Utilizing both ground truths, the author studies several U-Net and its' variant architectures with and without tumor edges ground truth as a target along with the tumor ground truth for brain tumor segmentation. The author used the BraTS2020 benchmark dataset to perform the study and the results are tabulated for the dice and Hausdorff95 metrics. The mean and median metrics are calculated for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) regions. Compared to the baseline U-Net and its variants, the models that learned edges along with the tumor regions performed well in core tumor regions in both training and validation datasets. The improved performance of edge-trained models trained on baseline models like U-Net and V-Net achieved performance similar to baseline state-of-the-art models like Swin U-Net and hybrid MR-U-Net. The edge-target trained models are capable of generating edge maps that can be useful for treatment planning. Additionally, for further explainability of the results, the activation map generated by the hybrid MR-U-Net has been studied.
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