TotalSegmentator
PulseAugur coverage of TotalSegmentator — every cluster mentioning TotalSegmentator across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New pipeline improves colon segmentation accuracy in 3D CT scans
Researchers have developed a novel three-stage pipeline designed to improve the accuracy and anatomical continuity of colon segmentation in 3D abdominal CT scans. This method addresses the common issue of disconnected p…
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AI improves medical imaging segmentation for cancer trials
Researchers have developed a method to improve the accuracy of deep learning models for segmenting clinical target volumes (CTVs) in medical imaging, specifically for the AGITG TOPGEAR clinical trial involving gastric c…
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Mamba architecture adapted for MRI-to-CT synthesis in radiotherapy planning
Researchers have adapted the SegMamba architecture, originally designed for image segmentation, to perform MRI-to-CT synthesis for radiotherapy planning. This novel approach utilizes state-space modeling to capture comp…
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New AI Model Predicts Patient and Imaging Details from CT/MR Scans
Researchers have developed a novel open-source model capable of predicting patient and acquisition characteristics directly from CT and MR images. This model, integrated into the TotalSegmentator framework, utilizes sep…
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New method boosts whole-heart segmentation accuracy across medical imaging sites
Researchers have developed a new method to improve whole-heart segmentation in medical imaging, specifically for computed tomography (CT) and magnetic resonance imaging (MRI) scans. This technique addresses the challeng…
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New methods boost whole-heart segmentation accuracy in CT and MRI
Researchers have developed new methods to improve whole-heart segmentation in medical imaging, specifically for computed tomography (CT) and magnetic resonance imaging (MRI). One approach, proposed by Purdue-M2, uses a …
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LoRA fine-tuning of MedSAM3 requires minimal annotations for medical image segmentation
A new study explores the effectiveness of LoRA fine-tuning for medical image segmentation using the MedSAM3 foundation model. Researchers found that with as few as 10 annotated cases, LoRA-adapted models achieved perfor…
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AI model enhances CT-less PET attenuation correction for medical imaging
Researchers have developed a novel approach for CT-less PET attenuation correction by synthesizing multimodal pseudo-CT images. Their method, submitted to the BIC-MAC 2026 Challenge, utilizes a modified nnU-Net architec…
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New method improves whole-body MRI registration for UK Biobank data
Researchers have developed a new method for registering whole-body MRI images from the UK Biobank, a large-scale health data study. This technique utilizes tissue masks, specifically subcutaneous adipose tissue and musc…
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MedSAM2-Anatomy framework boosts medical image segmentation accuracy
Researchers have developed MedSAM2-Anatomy, a novel framework designed to enhance the accuracy of musculoskeletal segmentation in medical imaging without requiring model retraining or manual input. This method leverages…
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New framework adapts CT foundation models for better radiology report alignment
Researchers have developed Anatomy Contextualized Adaptation (ACA), a novel framework designed to improve CT vision-language foundation models. ACA efficiently adapts existing frozen models for anatomy-level alignment w…
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New U-Net model offers efficient spine CT segmentation for edge devices
Researchers have developed SpineContextResUNet, a new 3D Residual U-Net architecture designed for efficient segmentation of spinal CT scans. This model addresses the high computational demands of existing methods by usi…