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Found 5 papers, 2 papers with code
Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings
For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips.
FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset
Through the \textit{Fetoscopic Placental Vessel Segmentation and Registration (FetReg)} challenge, we present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.
Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking
The latter, a combination of deep learning with optical flow tracking, yields an average balanced accuracy of 78. 2% across all the validated datasets.
Retrieval and Registration of Long-Range Overlapping Frames for Scalable Mosaicking of In Vivo Fetoscopy
Robustness to visual challenges during registration and long-range temporal consistency are proposed, offering first positive results on in vivo data for which standard mosaicking techniques are not applicable.
ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools
We propose the use of parametric rectified linear units for semantic labeling in these small architectures to increase the regularization ability of the design and maintain the segmentation accuracy without overfitting the training sets.
