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Found 9 papers, 5 papers with code
Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection
Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images.
Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits
It focuses on the visualization of the occurrence of phases, phase transitions, instruments, and instrument combinations across sets.
Comparison of Depth Estimation Setups from Stereo Endoscopy and Optical Tracking for Point Measurements
To support minimally-invasive intraoperative mitral valve repair, quantitative measurements from the valve can be obtained using an infra-red tracked stylus.
Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks
This is an improvement of $25\%$ in Dice for RV in comparison to direct application on axial slices.
Heatmap-based 2D Landmark Detection with a Varying Number of Landmarks
Mitral valve repair is a surgery to restore the function of the mitral valve.
How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?
Our results confirm that current deep learning models can achieve excellent results (left ventricle dice of $0. 951\pm{0. 003}$/$0. 941\pm{0. 007}$ train/validation) within a single data collection.
Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training
Such concepts are able to generate realistic representations of phantoms learned from real intraoperative endoscopic sequences.
Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries
Though, application of this approach to continuous video frames can result in flickering, which turned out to be especially prominent for this application.
Computers and Society
Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features
We evaluated our method on the ACDC dataset (4 pathology groups, 1 healthy group) and achieve dice scores of 0. 945 (LVC), 0. 908 (RVC) and 0. 905 (LVM) in a cross-validation over the training set (100 cases) and 0. 950 (LVC), 0. 923 (RVC) and 0. 911 (LVM) on the test set (50 cases).
