关键词: 医学影像, 脑卒中病灶分割, 半监督学习, 注意力机制, 不确定性估计

Abstract:

The automatic segmentation of brain lesions provides a reliable basis for the timely diagnosis and treatment of stroke patients and the formulation of diagnosis and treatment plans， but obtaining large-scale labeled data is expensive and time-consuming. Semi-Supervised Learning （SSL） methods alleviate this problem by utilizing a large number of unlabeled images and a limited number of labeled images. Aiming at the two problems of pseudo-label noise in SSL and the lack of ability of existing Three-Dimensional （3D） networks to focus on smaller objects， a semi-supervised method was proposed， namely， a rectified cross pseudo supervised method with attention mechanism for stroke lesion segmentation RPE-CPS （Rectified Cross Pseudo Supervision with Project & Excite modules）. First， the data was input into two 3D U-Net segmentation networks with the same structure but different initializations， and the obtained pseudo-segmentation graphs were used for cross-supervised training of the segmentation networks， making full use of the pseudo-label data to expand the training set， and encouraging a high similarity between the predictions of different initialized networks for the same input image. Second， a correction strategy about cross-pseudo-supervised approach based on uncertainty estimation was designed to reduce the impact of the noise in pseudo-labels. Finally， in the segmentation network of 3D U-Net， in order to improve the segmentation performance of small object classes， Project & Excite （PE） modules were added behind each encoder module， decoder module and bottleneck module. In order to verify the effectiveness of the proposed method， evaluation experiments were carried out on the Acute Ischemic Stroke （AIS） dataset of the cooperative hospital and the Ischemic Stroke Lesion Segmentation Challenge （ISLES2022） dataset. The experimental results showed that when only using 20% of the labeled data in the training set， the Dice Similarity Coefficient （DSC）， 95% Hausdorff Distance （HD95）， and Average Surface Distance （ASD） on the public dataset ISLES2022 reached 73.87%， 6.08 mm and 1.31 mm； on the AIS dataset， DSC， HD95， and ASD reached 67.74%， 15.38 mm and 1.05 mm， respectively. Compared with the state-of-the-art semi-supervised method Uncertainty Rectified Pyramid Consistency（URPC）， DSC improved by 2.19 and 3.43 percentage points， respectively. The proposed method can effectively utilize unlabeled data to improve segmentation accuracy， outperforms other semi-supervised methods， and is robust.

Key words: medical image, stroke lesion segmentation, Semi-Supervised Learning (SSL), attention mechanism, uncertainty estimation