ArXiv Preprint
Pancreatic cancer is one of the leading causes of cancer-related death.
Accurate detection, segmentation, and differential diagnosis of the full
taxonomy of pancreatic lesions, i.e., normal, seven major types of lesions, and
other lesions, is critical to aid the clinical decision-making of patient
management and treatment. However, existing works focus on segmentation and
classification for very specific lesion types (PDAC) or groups. Moreover, none
of the previous work considers using lesion prevalence-related non-imaging
patient information to assist the differential diagnosis. To this end, we
develop a meta-information-aware dual-path transformer and exploit the
feasibility of classification and segmentation of the full taxonomy of
pancreatic lesions. Specifically, the proposed method consists of a CNN-based
segmentation path (S-path) and a transformer-based classification path
(C-path). The S-path focuses on initial feature extraction by semantic
segmentation using a UNet-based network. The C-path utilizes both the extracted
features and meta-information for patient-level classification based on stacks
of dual-path transformer blocks that enhance the modeling of global contextual
information. A large-scale multi-phase CT dataset of 3,096 patients with
pathology-confirmed pancreatic lesion class labels, voxel-wise manual
annotations of lesions from radiologists, and patient meta-information, was
collected for training and evaluations. Our results show that our method can
enable accurate classification and segmentation of the full taxonomy of
pancreatic lesions, approaching the accuracy of the radiologist's report and
significantly outperforming previous baselines. Results also show that adding
the common meta-information, i.e., gender and age, can boost the model's
performance, thus demonstrating the importance of meta-information for aiding
pancreatic disease diagnosis.
Bo Zhou, Yingda Xia, Jiawen Yao, Le Lu, Jingren Zhou, Chi Liu, James S. Duncan, Ling Zhang
2023-03-02
