论文标题
深度像素的生成和聚类，用于在MR图像中弱监督脑肿瘤的细分
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images
论文作者
论文摘要
培训机器学习模型以分割肿瘤和医学图像中的其他异常是开发诊断工具的重要步骤，但通常需要手动注释的地面真实分段，这需要大量的时间和资源。这项工作建议使用超像素生成模型和超像素聚类模型来实现弱监督的脑肿瘤分割。所提出的方法利用了易于访问的二进制图像级分类标签，以显着改善由标准弱监督方法生成的初始区域，而无需地面真相注释。我们使用了来自多模式脑肿瘤分割挑战2020数据集的2D磁共振脑扫描，标签表明存在训练管道的肿瘤。在测试队列上，我们的方法达到的平均骰子系数为0.691，平均95％的Hausdorff距离为18.1，表现优于现有的基于超级像素的弱监督分割方法。
Training machine learning models to segment tumors and other anomalies in medical images is an important step for developing diagnostic tools but generally requires manually annotated ground truth segmentations, which necessitates significant time and resources. This work proposes the use of a superpixel generation model and a superpixel clustering model to enable weakly supervised brain tumor segmentations. The proposed method utilizes binary image-level classification labels, which are readily accessible, to significantly improve the initial region of interest segmentations generated by standard weakly supervised methods without requiring ground truth annotations. We used 2D slices of magnetic resonance brain scans from the Multimodal Brain Tumor Segmentation Challenge 2020 dataset and labels indicating the presence of tumors to train the pipeline. On the test cohort, our method achieved a mean Dice coefficient of 0.691 and a mean 95% Hausdorff distance of 18.1, outperforming existing superpixel-based weakly supervised segmentation methods.
