  Automatically measuring lesion/tumor size with RECIST (Response Evaluation
Criteria In Solid Tumors) diameters and segmentation is important for
computer-aided diagnosis. Although it has been studied in recent years, there
is still space to improve its accuracy and robustness, such as (1) enhancing
features by incorporating rich contextual information while keeping a high
spatial resolution and (2) involving new tasks and losses for joint
optimization. To reach this goal, this paper proposes a transformer-based
network (MeaFormer, Measurement transFormer) for lesion RECIST diameter
prediction and segmentation (LRDPS). It is formulated as three correlative and
complementary tasks: lesion segmentation, heatmap prediction, and keypoint
regression. To the best of our knowledge, it is the first time to use keypoint
regression for RECIST diameter prediction. MeaFormer can enhance
high-resolution features by employing transformers to capture their long-range
dependencies. Two consistency losses are introduced to explicitly build
relationships among these tasks for better optimization. Experiments show that
MeaFormer achieves the state-of-the-art performance of LRDPS on the large-scale
DeepLesion dataset and produces promising results of two downstream
clinic-relevant tasks, i.e., 3D lesion segmentation and RECIST assessment in
longitudinal studies.
