2017 Robotic Instrument Segmentation Challenge

By M. Allan, Alexey A. Shvets, T. Kurmann, Rahul Duggal, Yun-Hsuan Su, Nicola Rieke, Iro Laina, Niveditha Kalavakonda, S. Bodenstedt, Luis C. García-Peraza and 8 more. arXiv.org, 2019

310 citations recorded by the source corpus.

In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison. However, this type of approach has had limited translation to problems in robotic assisted surgery as this field has never established the same level of common datasets and benchmarking methods. In 2015 a sub-challenge was introduced at the EndoVis workshop where a set of robotic images were provided with automatically generated annotations from robot forward kinematics. However, there were issues with this dataset due to the limited background variation, lack of complex motion and inaccuracies in the annotation. In this work we present the results of the 2017 challenge on robotic instrument segmentation which involved 10 teams participating in binary, parts and type based segmentation of articulated da Vinci robotic instruments.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e705aeaa9440744c8341b14c7404f6a91e694571

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In Biomedical Engineering

1. Jun Ma
2. Bo Wang

In Computer Vision and Pattern Recognition

1. Feifei Li
2. Jun Ma
3. Bo Wang

Citations in the corpus

• Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining (2024, unknown): “We evaluate the performance and scalability of Swin-UMamba across three distinct medical image segmentation datasets, encompassing AbdomenMRI [12] (abdominal organs), Endoscopy [1] (instruments), and Microscopy [19] (cell).”
• U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation (2024, methodology): “Endoscopy images: This dataset was from the MICCAI 2017 EndoVis Challenge [1] that focused on segmenting seven instruments from endoscopy images, including the large needle driver, prograsp forceps, monopolar curved scissors, cadiere forceps, bipolar forceps, vessel sealer, and additionally a…”