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Found 19 papers, 5 papers with code
Boundary-Emphasized Weight Maps for Distal Airway Segmentation
Automated airway segmentation from lung CT scans is vital for diagnosing and monitoring pulmonary diseases.
SINETRA: a Versatile Framework for Evaluating Single Neuron Tracking in Behaving Animals
Accurately tracking neuronal activity in behaving animals presents significant challenges due to complex motions and background noise.
Curriculum Learning for Few-Shot Domain Adaptation in CT-based Airway Tree Segmentation
Despite advances with deep learning (DL), automated airway segmentation from chest CT scans continues to face challenges in segmentation quality and generalization across cohorts.
Dense Self-Supervised Learning for Medical Image Segmentation
The time, cost and expertise required to label images at the pixel-level for each new task has slowed down widespread adoption of the paradigm.
Deep ContourFlow: Advancing Active Contours with Deep Learning
This paper introduces a novel approach that combines unsupervised active contour models with deep learning for robust and adaptive image segmentation.
Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors
We then incorporate these sparse representations in a standard supervised segmentation pipeline as a pretraining step to enhance the performance of the DL models.
Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation
One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a set of expert-level annotations while attaining good segmentation performance.
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a $\textbf{unified}$ UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation.
Cardiac Adipose Tissue Segmentation via Image-Level Annotations
To lessen the need for pixel-wise labeling, we develop a two-stage deep learning framework for cardiac adipose tissue segmentation using image-level annotations on OCT images of human cardiac substrates.
Suggestive Annotation of Brain MR Images with Gradient-guided Sampling
We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans
We introduce a recursive refinement network (RRN) for unsupervised medical image registration, to extract multi-scale features, construct normalized local cost correlation volume and recursively refine volumetric deformation vector fields.
Ranked #1 on
Image Registration
on DIR-LAB COPDgene
Co-Seg: An Image Segmentation Framework Against Label Corruption
Supervised deep learning performance is heavily tied to the availability of high-quality labels for training.
Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks.
Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention
Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0. 27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices).
Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction
Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity.
Transfer Learning from Partial Annotations for Whole Brain Segmentation
Brain MR image segmentation is a key task in neuroimaging studies.
SAPSAM - Sparsely Annotated Pathological Sign Activation Maps - A novel approach to train Convolutional Neural Networks on lung CT scans using binary labels only
Chronic Pulmonary Aspergillosis (CPA) is a complex lung disease caused by infection with Aspergillus.
Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation
Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success.
