REVIEW 2 cited by
ISINet: An Instance-Based Approach for Surgical Instrument Segmentation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We study the task of semantic segmentation of surgical instruments in robotic-assisted surgery scenes. We propose the Instance-based Surgical Instrument Segmentation Network (ISINet), a method that addresses this task from an instance-based segmentation perspective. Our method includes a temporal consistency module that takes into account the previously overlooked and inherent temporal information of the problem. We validate our approach on the existing benchmark for the task, the Endoscopic Vision 2017 Robotic Instrument Segmentation Dataset, and on the 2018 version of the dataset, whose annotations we extended for the fine-grained version of instrument segmentation. Our results show that ISINet significantly outperforms state-of-the-art methods, with our baseline version duplicating the Intersection over Union (IoU) of previous methods and our complete model triplicating the IoU.
Forward citations
Cited by 2 Pith papers
-
CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning
A CLIP-based encoder with RL residual refinement and curriculum learning reaches 81% mIoU on EndoVis 2018 and 74.12% on EndoVis 2017 surgical segmentation.
-
Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation
A dual SegFormer pipeline with confidence-based fusion achieves 0.80 mIoU on EndoVis2018 holistic segmentation but lags prompt-based models on EndoVis2017.
Discussion (0). Sign in to comment.