In clinical settings, intracranial hemorrhages (ICH) are routinely diagnosed using non-contrast CT (NCCT) for severity assessment. Accurate automated segmentation of ICH lesions is the initial and essential step, immensely useful for such assessment. However, compared to other structural imaging modalities such as MRI, in NCCT images ICH appears with very low contrast and poor SNR. Over recent years, deep learning (DL)-based methods have shown great potential, however, training them requires a huge amount of manually annotated lesion-level labels, with sufficient diversity to capture the characteristics of ICH. In this work, we propose a novel weakly supervised DL method for ICH segmentation on NCCT scans, using image-level binary classification labels, which are less time-consuming and labor-efficient when compared to the manual labeling of individual ICH lesions. Our method initially determines the approximate location of ICH using class activation maps from a classification network, which is trained to learn dependencies across contiguous slices. We further refine the ICH segmentation using pseudo-ICH masks obtained in an unsupervised manner. The method is flexible and uses a computationally light architecture during testing. On evaluating our method on the validation data of the MICCAI 2022 INSTANCE challenge, our method achieves a Dice value of 0.55, comparable with those of existing weakly supervised method (Dice value of 0.47), despite training on a much smaller training data.

翻译：在临床环境中，颅内出血（ICH）通常通过非增强CT（NCCT）进行严重程度评估。准确自动分割ICH病灶是初始且关键的步骤，对该评估具有极大帮助。然而，与MRI等其他结构性成像模式相比，NCCT图像中的ICH呈现极低对比度和信噪比。近年来，基于深度学习（DL）的方法展现出巨大潜力，但其训练需要大量人工标注的病灶级标签，且标签需具备足够多样性以捕捉ICH特征。本研究提出了一种新颖的弱监督DL方法，用于在NCCT扫描中实现ICH分割，仅使用图像级二分类标签——与人工逐例标注ICH病灶相比，此类标签更省时省力。该方法首先通过分类网络生成的类激活图初步确定ICH大致位置，该分类网络经过训练以学习连续切片间的依赖关系；随后利用无监督方式获取的伪ICH掩膜进一步优化分割结果。该方法灵活且测试时采用轻量级计算架构。在MICCAI 2022 INSTANCE挑战赛验证数据上的评估显示，尽管训练数据规模更小，本方法仍达到0.55的Dice值，与现有弱监督方法（Dice值为0.47）性能相当。