Classification of Pancreatic Cystic Neoplasms Based on Multimodality Images

摘要

Classification of pancreatic cystic neoplasms (PCN) into sub-classes is crucial since their treatments are different. However, accurate classification is very difficult even for radiologists, due to similar appearance and shape. We propose a network called PCN-Net which makes use of T1/T2 MRI of abdomen by its three stages design. The first and second stages are trained on T1 and T2 separately for detection and inter-modality registration. After a Z-Continuity Filter and modalities fusion, the third stage predict the results with registered image pairs. On a database of 48 patients, our method can predict with slice level accuracy of 80.0% and patient level accuracy of 92.3%, which are much better than other baseline methods.