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Found 16 papers, 14 papers with code
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
Generative Medical Segmentation
Concretely, GMS employs a robust pre-trained Variational Autoencoder (VAE) to derive latent representations of both images and masks, followed by a mapping model that learns the transition from image to mask in the latent space.
MatchSeg: Towards Better Segmentation via Reference Image Matching
Few-shot learning aims to overcome the need for annotated data by using a small labeled dataset, known as a support set, to guide predicting labels for new, unlabeled images, known as the query set.
RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images
This study introduces a novel framework for enhancing domain generalization in medical imaging, specifically focusing on utilizing unlabelled multi-view colour fundus photographs.
DDSB: An Unsupervised and Training-free Method for Phase Detection in Echocardiography
Accurate identification of End-Diastolic (ED) and End-Systolic (ES) frames is key for cardiac function assessment through echocardiography.
Rethinking Low-quality Optical Flow in Unsupervised Surgical Instrument Segmentation
Video-based surgical instrument segmentation plays an important role in robot-assisted surgeries.
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems.
ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance
In this paper, we propose a foreground harmonization framework (ARHNet) to tackle intensity disparities and make synthetic images look more realistic.
SKiT: a Fast Key Information Video Transformer for Online Surgical Phase Recognition
We highlight that the inference time of SKiT is constant, and independent from the input length, making it a stable choice for keeping a record of important global information, that appears on long surgical videos, essential for phase recognition.
MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation
Accurate stroke lesion segmentation plays a pivotal role in stroke rehabilitation research, to provide lesion shape and size information which can be used for quantification of the extent of the stroke and to assess treatment efficacy.
Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder
Concretely, our framework consists of two sequential networks: a mask synthesis network and a mask-guided lesion synthesis network.
Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures
We demonstrate that an STCNN trained on a HAR dataset can be used in combination with an RNN to accurately represent arbitrarily long videos of seizures.
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections
We fine-tuned our model on three small annotated datasets from different institutions and on the annotated images in EPISURG, comprising 20, 33, 19 and 133 subjects.
Enhancing Fiber Orientation Distributions using convolutional Neural Networks
We evaluate how well each CNN model can resolve local fiber orientation 1) when training and testing on datasets with the same dMRI acquisition protocol; 2) when testing on a dataset with a different dMRI acquisition protocol than used to train the CNN models; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN models.
Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning
In addition to EPISURG, we used three public datasets comprising 1813 preoperative MR images for training.
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Additionally, we provide a graphical interface within a TorchIO extension in 3D Slicer to visualize the effects of transforms.
