  Measuring lesion size is an important step to assess tumor growth and monitor
disease progression and therapy response in oncology image analysis. Although
it is tedious and highly time-consuming, radiologists have to work on this task
by using RECIST criteria (Response Evaluation Criteria In Solid Tumors)
routinely and manually. Even though lesion segmentation may be the more
accurate and clinically more valuable means, physicians can not manually
segment lesions as now since much more heavy laboring will be required. In this
paper, we present a prior-guided dual-path network (PDNet) to segment common
types of lesions throughout the whole body and predict their RECIST diameters
accurately and automatically. Similar to [1], a click guidance from
radiologists is the only requirement. There are two key characteristics in
PDNet: 1) Learning lesion-specific attention matrices in parallel from the
click prior information by the proposed prior encoder, named click-driven
attention; 2) Aggregating the extracted multi-scale features comprehensively by
introducing top-down and bottom-up connections in the proposed decoder, named
dual-path connection. Experiments show the superiority of our proposed PDNet in
lesion segmentation and RECIST diameter prediction using the DeepLesion dataset
and an external test set. PDNet learns comprehensive and representative deep
image features for our tasks and produces more accurate results on both lesion
segmentation and RECIST diameter prediction.
