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This work makes significant strides in the field of monoocular endoscopic depth perception, drastically improving on previous methods. The task of monocular depth perception demonstrates a nuanced understanding of the surgical scene, and could act as a vital building block in future technologies. To acheive our results, we leverage large vision transformers trained on huge natural image datasets and fine tuned to our ensembled meta dataset of sugical videos. Read more about it in our pre-print, or get our model here.
We have recently published the paper “Garcia-Peraza-Herrera, L. C., Fidon, L., DEttorre, C. Stoyanov, D., Vercauteren, T., Ourselin, S. (2021). Image Compositing for Segmentation of Surgical Tools without Manual Annotations. Transactions in Medical Imaging (📖)”. Inspired by special effects, we introduce a novel deep-learning method to segment surgical instruments in endoscopic images.
