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.

翻译：训练机器学习模型对医学图像中的肿瘤及其他异常区域进行分割是诊断工具开发的重要步骤，但通常需要人工标注的真实分割结果，这耗费大量时间与资源。本研究提出采用超像素生成模型与超像素聚类模型实现脑肿瘤的弱监督分割。该方法利用易于获取的二值图像级分类标签，在不依赖真实标注的情况下，显著改善标准弱监督方法生成的初始感兴趣区域分割结果。我们使用来自多模态脑肿瘤分割挑战2020数据集的二维磁共振脑扫描切片及标注肿瘤存在性的标签对流程进行训练。在测试队列上，该方法实现了平均Dice系数0.691和平均95%豪斯多夫距离18.1，优于现有基于超像素的弱监督分割方法。