nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
PulseAugur coverage of nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation — every cluster mentioning nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation across labs, papers, and developer communities, ranked by signal.
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AI framework Renal-Net improves renal mass segmentation on CT scans
Researchers have developed Renal-Net, an AI framework for segmenting renal masses on CT scans, aiming to improve objective assessment of kidney volume and lesions. The algorithm, built using the nnU-Net framework and tr…
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Synthetic MRI data boosts automated FCD detection
Researchers have developed a method using conditional generative networks to create synthetic MRI images of focal cortical dysplasia (FCD). These synthetic images were found to be realistic enough that experts could bar…
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New metric SC-MFJ assesses haptic quality in medical image segmentation
Researchers have developed a new metric called SC-MFJ to evaluate the haptic quality of medical image segmentations, which is crucial for surgical simulations. Unlike traditional metrics that focus on geometric overlap,…
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New framework RAMP boosts CT segmentation model robustness
Researchers have developed a new framework called RAMP to improve the robustness of deep learning models used for CT image segmentation. This framework addresses the issue of performance degradation when models encounte…
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SegGuidedNet improves brain tumor segmentation with attention supervision
Researchers have developed SegGuidedNet, a novel 3D neural network designed for more accurate and interpretable brain tumor segmentation from MRI scans. The network incorporates a SegAttentionGate module that supervises…
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Research paper distinguishes cross-validation from deep ensembles for AI uncertainty
A new research paper titled "Lost in the Folds" highlights a common misunderstanding in AI research regarding uncertainty estimation in medical image segmentation. The study reveals that using K-fold cross-validation (C…
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AI models struggle with unseen PET/CT tracer combinations despite segmentation gains
The autoPET3 challenge, held in conjunction with MICCAI 2024, focused on automated lesion segmentation in whole-body PET/CT scans, specifically testing compositional generalization. The challenge utilized a large datase…
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Sparse convolutional networks boost 3D kidney tumor segmentation accuracy and speed
Researchers have developed a novel two-stage 3D segmentation method using submanifold sparse convolutional networks (SSCNs) for more efficient and accurate kidney tumor detection in CT scans. This approach first identif…
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LM-CartSeg pipeline automates knee MRI cartilage and bone segmentation for radiomics
Researchers have developed LM-CartSeg, an automated pipeline for segmenting knee MRI scans to analyze cartilage and subchondral bone. This system uses two 3D nnU-Net models and geometric rules for accurate compartmental…
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Primus V2 Transformer architecture sets new state-of-the-art in 3D medical image segmentation
Researchers have developed Primus and PrimusV2, novel Transformer-centric architectures for 3D medical image segmentation that outperform hybrid models. These new architectures address shortcomings in current Transforme…
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Deep learning models segment peritoneal cancer regions in CT scans
Researchers have developed a deep learning method to automatically segment regions for the radiological Peritoneal Cancer Index (rPCI) from CT scans. The study evaluated nnU-Net and Swin UNETR on 62 CT scans, with nnU-N…
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New CATMIL method improves brain MRI lesion segmentation accuracy
Researchers have developed a new objective function called CATMIL to improve the segmentation of small structures in brain MRI scans. This method combines standard segmentation loss with auxiliary terms that adaptively …