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Found 18 papers, 11 papers with code
Scaling nnU-Net for CBCT Segmentation
This paper presents our approach to scaling the nnU-Net framework for multi-structure segmentation on Cone Beam Computed Tomography (CBCT) images, specifically in the scope of the ToothFairy2 Challenge.
RadioActive: 3D Radiological Interactive Segmentation Benchmark
By open-sourcing RadioActive, we invite the research community to integrate their models and prompting techniques, ensuring continuous and transparent evaluation of interactive segmentation models in 3D medical imaging.
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.
Revisiting MAE pre-training for 3D medical image segmentation
Self-Supervised Learning (SSL) presents an exciting opportunity to unlock the potential of vast, untapped clinical datasets, for various downstream applications that suffer from the scarcity of labeled data.
Decoupling Semantic Similarity from Spatial Alignment for Neural Networks
In this paper, we propose to solve this through semantic RSMs, which are invariant to spatial permutation.
Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting
Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy.
Data-Centric Strategies for Overcoming PET/CT Heterogeneity: Insights from the AutoPET III Lesion Segmentation Challenge
The third autoPET challenge introduced a new data-centric task this year, shifting the focus from model development to improving metastatic lesion segmentation on PET/CT images through data quality and handling strategies.
From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging
Automated lesion segmentation in PET/CT scans is crucial for improving clinical workflows and advancing cancer diagnostics.
Mitigating False Predictions In Unreasonable Body Regions
This limitation leads to false predictions when applied to body regions beyond the FOV of the training data.
nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation
The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art results.
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures
Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision.
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement
Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision.
Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency
Given an ensemble of independently trained models, this results in correlated predictions and common failure modes.
Transformer Utilization in Medical Image Segmentation Networks
Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation.
MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation
Our findings offer a new direction for the medical imaging community to effectively utilize the wealth of available data for improved segmentation performance.
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation.
Ranked #1 on
Medical Image Segmentation
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Extending nnU-Net is all you need
Semantic segmentation is one of the most popular research areas in medical image computing.
