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Found 25 papers, 15 papers with code
Unconditional Latent Diffusion Models Memorize Patient Imaging Data
Generative latent diffusion models hold a wide range of applications in the medical imaging domain.
Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis
Despite the promise, the capacity of such models to memorize sensitive patient training data and synthesize samples showing high resemblance to training data samples is relatively unexplored.
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.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
Furthermore, we demonstrate that synthetic images can be used in a self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (dice score 0. 91 vs. 0. 95 without vs. with synthetic data).
Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations
Simultaneously, deep learning-based deformable image registration is able to estimate discrete vector fields which warp one time step of a CMR sequence to the following in a self-supervised manner.
Content-Aware Differential Privacy with Conditional Invertible Neural Networks
Manipulation of the latent space leads to a modified image while preserving important details.
mvHOTA: A multi-view higher order tracking accuracy metric to measure spatial and temporal associations in multi-point detection
The main evaluation metric available comes from Multi-object tracking (MOT) methods to benchmark performance on datasets such as KITTI with the recently proposed higher order tracking accuracy (HOTA) metric, which is capable of providing a better description of the performance over metrics such as MOTA, DetA, and IDF1.
Posterior temperature optimized Bayesian models for inverse problems in medical imaging
In POTOBIM, we optimize both the parameters of the prior distribution and the posterior temperature with respect to reconstruction accuracy using Bayesian optimization with Gaussian process regression.
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.
Comparison of Evaluation Metrics for Landmark Detection in CMR Images
In this work, we extended the public ACDC dataset with additional labels of the right ventricular insertion points and compare different variants of a heatmap-based landmark detection pipeline.
Point detection through multi-instance deep heatmap regression for sutures in endoscopy
Method: In this work, we formulate the suture detection task as a multi-instance deep heatmap regression problem, to identify entry and exit points of sutures.
Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation
In this use case, it is of paramount importance to display objects like needles, sutures or instruments consistent in both domains while altering the style to a more tissue-like appearance.
Posterior Temperature Optimization in Variational Inference for Inverse Problems
Bayesian methods feature useful properties for solving inverse problems, such as tomographic reconstruction.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
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.
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
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.
Towards Augmented Reality-based Suturing in Monocular Laparoscopic Training
Reliable 3D localization of needle and instruments in real time could be used to augment the scene with additional parameters that describe their quantitative geometric relation, e. g. the relation between the estimated needle plane and its rotation center and the instrument.
Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
We show that this data set can be used to train models for the task of liver segmentation of laparoscopic images.
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).
