  In surgical procedures, correct instrument counting is essential. Instance
segmentation is a location method that locates not only an object's bounding
box but also each pixel's specific details. However, obtaining mask-level
annotations is labor-intensive in instance segmentation. To address this issue,
we propose a novel yet effective weakly-supervised surgical instrument instance
segmentation approach, named Point-based Weakly-supervised Instance
Segmentation (PWISeg). PWISeg adopts an FCN-based architecture with
point-to-box and point-to-mask branches to model the relationships between
feature points and bounding boxes, as well as feature points and segmentation
masks on FPN, accomplishing instrument detection and segmentation jointly in a
single model. Since mask level annotations are hard to available in the real
world, for point-to-mask training, we introduce an unsupervised projection
loss, utilizing the projected relation between predicted masks and bboxes as
supervision signal. On the other hand, we annotate a few pixels as the key
pixel for each instrument. Based on this, we further propose a key pixel
association loss and a key pixel distribution loss, driving the point-to-mask
branch to generate more accurate segmentation predictions. To comprehensively
evaluate this task, we unveil a novel surgical instrument dataset with manual
annotations, setting up a benchmark for further research. Our comprehensive
research trial validated the superior performance of our PWISeg. The results
show that the accuracy of surgical instrument segmentation is improved,
surpassing most methods of instance segmentation via weakly supervised bounding
boxes. This improvement is consistently observed in our proposed dataset and
when applied to the public HOSPI-Tools dataset.
