IEEE J Biomed Health Inform. 2023 Jul;27(7):3372-3383. doi: 10.1109/JBHI.2023.3270861. Epub 2023 Jun 30.
Segmenting stroke lesions and assessing the thrombolysis in cerebral infarction (TICI) grade are two important but challenging prerequisites for an auxiliary diagnosis of the stroke. However, most previous studies have focused only on a single one of two tasks, without considering the relation between them. In our study, we propose a simulated quantum mechanics-based joint learning network (SQMLP-net) that simultaneously segments a stroke lesion and assesses the TICI grade. The correlation and heterogeneity between the two tasks are tackled with a single-input double-output hybrid network. SQMLP-net has a segmentation branch and a classification branch. These two branches share an encoder, which extracts and shares the spatial and global semantic information for the segmentation and classification tasks. Both tasks are optimized by a novel joint loss function that learns the intra- and inter-task weights between these two tasks. Finally, we evaluate SQMLP-net with a public stroke dataset (ATLAS R2.0). SQMLP-net obtains state-of-the-art metrics (Dice:70.98% and accuracy:86.78%) and outperforms single-task and existing advanced methods. An analysis found a negative correlation between the severity of TICI grading and the accuracy of stroke lesion segmentation.
分割脑梗死病灶和评估血栓溶解（TICI）分级是卒中辅助诊断的两个重要但具有挑战性的前提条件。然而，之前的大多数研究都只关注这两个任务中的一个，而没有考虑它们之间的关系。在我们的研究中，我们提出了一种基于模拟量子力学的联合学习网络（SQMLP-net），该网络可以同时分割脑梗死病灶并评估 TICI 分级。采用单输入双输出混合网络来处理两个任务之间的相关性和异质性。SQMLP-net 有一个分割分支和一个分类分支。这两个分支共享一个编码器，用于提取和共享分割和分类任务的空间和全局语义信息。这两个任务通过一个新的联合损失函数进行优化，该函数学习两个任务之间的内部和外部任务权重。最后，我们使用一个公共的卒中数据集（ATLAS R2.0）来评估 SQMLP-net。SQMLP-net 获得了最先进的指标（Dice：70.98%和准确性：86.78%），优于单任务和现有先进方法。分析发现 TICI 分级严重程度与脑梗死病灶分割准确性之间存在负相关。
