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Found 21 papers, 9 papers with code
The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark
This technical report provides a detailed overview of Endoscapes, a dataset of laparoscopic cholecystectomy (LC) videos with highly intricate annotations targeted at automated assessment of the Critical View of Safety (CVS).
Challenges in Multi-centric Generalization: Phase and Step Recognition in Roux-en-Y Gastric Bypass Surgery
The use of multi-centric training data, experiments 6) and 7), improves the generalization capabilities of the models, bringing them beyond the level of independent mono-centric training and validation (experiments 1) and 2)).
TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research
To validate the dataset's utility, 5 competitive Deep Learning models for automatic kidney segmentation were benchmarked, yielding average DICE scores from 83. 2% to 89. 1% for CT, and 61. 9% to 79. 4% for US images.
Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition
In this work, we propose to use coarser and easier-to-annotate activity labels, namely phases, as weak supervision to learn step recognition with fewer step annotated videos.
Live Laparoscopic Video Retrieval with Compressed Uncertainty
Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care.
Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation
A major obstacle to building models for effective semantic segmentation, and particularly video semantic segmentation, is a lack of large and well annotated datasets.
Ranked #2 on
Semantic Segmentation
on Endoscapes
Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos
To achieve this task, we introduce our new model, the Rendezvous (RDV), which recognizes triplets directly from surgical videos by leveraging attention at two different levels.
Ranked #1 on
Action Triplet Recognition
on CholecT50
Intraoperative time out to promote the implementation of the critical view of safety in laparoscopic cholecystectomy: a video-based assessment of 343 procedures
The primary endpoint was to compare the rate of CVS achievement between LCs performed in the year before and the year after the 5-second rule.
Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures
Conclusion: In this work, we present a multi-task multi-stage temporal convolutional network for surgical activity recognition, which shows improved results compared to single-task models on the Bypass40 gastric bypass dataset with multi-level annotations.
Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets
Recognition of surgical activity is an essential component to develop context-aware decision support for the operating room.
Ranked #1 on
Action Triplet Recognition
on CholecT40
Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos
Results: We build a baseline tracker on top of the CNN model and demonstrate that our approach based on the ConvLSTM outperforms the baseline in tool presence detection, spatial localization, and motion tracking by over 5. 0%, 13. 9%, and 12. 6%, respectively.
Ranked #2 on
Surgical tool detection
on Cholec80
Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition
Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior.
Future-State Predicting LSTM for Early Surgery Type Recognition
This work presents a novel approach for the early recognition of the type of a laparoscopic surgery from its video.
Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos
We propose a deep architecture, trained solely on image level annotations, that can be used for both tool presence detection and localization in surgical videos.
Ranked #4 on
Surgical tool detection
on Cholec80
Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks
In this work, we propose a new self-supervised pre-training approach based on the prediction of remaining surgery duration (RSD) from laparoscopic videos.
RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations
In this paper, we propose a deep learning pipeline, referred to as RSDNet, which automatically estimates the remaining surgery duration (RSD) intraoperatively by using only visual information from laparoscopic videos.
Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016
On top of these architectures we propose to use two different approaches to enforce the temporal constraints of the surgical workflow: (1) HMM-based and (2) LSTM-based pipelines.
Single- and Multi-Task Architectures for Tool Presence Detection Challenge at M2CAI 2016
The tool presence detection challenge at M2CAI 2016 consists of identifying the presence/absence of seven surgical tools in the images of cholecystectomy videos.
Automatic View-Point Selection for Inter-Operative Endoscopic Surveillance
Esophageal adenocarcinoma arises from Barrett's esophagus, which is the most serious complication of gastroesophageal reflux disease.
ORBSLAM-based Endoscope Tracking and 3D Reconstruction
It is our first contribution to exploit ORBSLAM, one of the best performing monocular SLAM algorithms, to estimate both of the endoscope location, and 3D structure of the surgical scene.
EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
In the literature, two types of features are typically used to perform this task: visual features and tool usage signals.
Ranked #5 on
Surgical tool detection
on Cholec80
Offline surgical phase recognition
Online surgical phase recognition
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