Anatomical attention-based prediction of postoperative pulmonary venous obstruction via CTA images. (January 2023) - Pei, Yuchen; Shi, Guocheng; Li, Jieyu; Sun, Dazhen; Wen, Chen; Li, Jiang; Huang, Meiping; Chen, Huiwen; Wang, Lisheng
Abstract: Total anomalous pulmonary venous connection (TAPVC) is a rare congenital heart disease, with which some patients suffer from postoperative pulmonary venous obstruction (PPVO), requiring particular follow-up strategies and treatments. PPVO prediction has important clinical significance, while building a PPVO prediction model is challenging due to limited data and class imbalance distribution. Inspired by the anatomical evidence of PPVO, which is related to the structure of the left atrium (LA) and pulmonary vein (PV), we design an effective multi-task network for PPVO classification. The proposed method incorporates clinical priors and merits of the segmentation-based network into the classification task. The features learned from segmenting LA and PV are concatenated into the PPVO classification branch to constrain the learning of discriminative features. Anatomical-guided attention is applied in the aggregation of these features to restrict them focusing on TAPVC-related regions. To deal with the imbalance classification problem of PPVO, a novel classification loss derived by masked class activation map (MCAM) is designed to improve the classification performance. Computed tomography angiography (CTA) images of 146 patients diagnosed with supracardiac TAPVC in Shanghai Children's Medical Center and Guangdong Provincial People's Hospital were enrolled in this work. The comprehensive experiments demonstrate the effectiveness and generalization of our proposed method. The automatic PPVO prediction model shows the potential application in helping clinicians develop follow-up strategies, thereby improving the survival rate of TAPVC patients. Highlights: The automatic prediction model is proposed to utilize the anatomical information of LA and PV for predicting the PPVO in CTA images of TAPVC patients. Anatomical-guided constraint as deep supervision is designed to constrain the classification branch to focus on TAPVC-related regions. The novel classification loss based on MCAM is proposed to address the imbalance classification problem and promote classification performance. The comprehensive experiments on multi-center datasets verify the effectiveness and generalization of our proposed method.