Huilin Cui

Area of research

Surgery · Pulmonary and Respiratory Medicine

Research interest

Research topics from publications: Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network; Could tumour volume and major and minor axis based on CTA statistical anatomy improve the pre‐operative T‐stage in oesophageal cancer?; Prostate zones and tumor morphological parameters on magnetic resonance imaging for predicting the tumor-stage diagnosis of prostate cancer. Representative work: OBJECTIVE: To create a deep-learning automatic segmentation model for esophageal cancer (EC), metastatic lymph nodes (MLNs) and their adjacent structures using the UperNet Swin network and computed tomography angiography (CTA) images and to improve the effectiveness and precision of EC automatic segmentation and TN stage diagnosis. METHODS: Attention U-Net, UperNet Swin, UNet++ and UNet were used to train the EC segmentation model to automatically segment the EC, esophagus, pericardium, aorta and MLN from CTA images of 182 patients with postoperative pathologically proven EC. The Dice similarity coefficient (DSC), sensitivity, and positive predictive value (PPV) were used to assess their seg Abstract Objectives To statistically study the 3D shape of oesophageal cancer (EC) and its spatial relationships based on computed tomography angiography (CTA) 3D reconstruction, to determine its relationship with T‐stages, and to create an optimal T‐stage diagnosis protocol based on CTA calculation. Methods Pre‐operative CTA images of 155 patients with EC were retrospectively collected and divided into four groups: T1–T4. We used Amira software to segment and 3D reconstruct the EC, oesophagus, aorta, pericardium and peripheral lymph nodes and measured their surface area, volume, major axis, minor axis, longitudinal length, roughness and relationship to the aorta of the EC. One‐way ANOVA, in