Polyp and Surgical Instrument Segmentation with Double Encoder-Decoder Networks
This work addresses a domain-specific medical imaging challenge for endoscopic analysis, but it is incremental as it builds on a previously applied method with enhancements.
The paper tackled the problem of segmenting polyps and surgical instruments from endoscopic images for the MedAI competition, resulting in segmentations that show good agreement with manual delineations by medical experts.
This paper describes a solution for the MedAI competition, in which participants were required to segment both polyps and surgical instruments from endoscopic images. Our approach relies on a double encoder-decoder neural network which we have previously applied for polyp segmentation, but with a series of enhancements: a more powerful encoder architecture, an improved optimization procedure, and the post-processing of segmentations based on tempered model ensembling. Experimental results show that our method produces segmentations that show a good agreement with manual delineations provided by medical experts.