Medicine & Health

463 items

arXiv Sep 26, 2026
The study proposes a software-only method that reconstructs a 3D oral model from only ten 2D intraoral images captured from different angles, requiring no dedicated hardware; the model is trained on the public Teeth3DS dataset of 950 upper jaw samples and uses MobileNetV2 as the image encoder with Multi-head Attention for multi-view feature fusion, achieving 77.49% accuracy under nearest-neighbor matching with a distance threshold of 0.035, while predicted vertices tend to concentrate in high-density regions of the ground truth, producing uneven point distribution in the reconstructed model.
The study proposes a software-only method that reconstructs a 3D oral model from only ten 2D intraoral images captured from different angles, requiring no dedicated hardware; the model is trained on the public Teeth3DS dataset of 950 upper jaw samples and uses MobileNetV2 as the image encoder with Multi-head Attention for multi-view feature fusion, achieving 77.49% accuracy under nearest-neighbor matching with a distance threshold of 0.035, while predicted vertices tend to concentrate in high-density regions of the ground truth, producing uneven point distribution in the reconstructed model.
Reconstructing 3D Oral Models from Just Ten 2D Intraoral Images Reaches 77.49% Nearest-Neighbor Accuracy on Teeth3DS
The study proposes a software-only method that reconstructs a 3D oral model from only ten 2D intraoral images captured from different angles, requiring no dedicated hardware; the model is trained on the public Teeth3DS dataset of 950 upper jaw samples and uses MobileNetV2 as the image encoder with Multi-head Attention for multi-view feature fusion, achieving 77.49% accuracy under nearest-neighbor matching with a distance threshold of 0.035, while predicted vertices tend to concentrate in high-density regions of the ground truth, producing uneven point distribution in the reconstructed model.
The study proposes a software-only method that reconstructs a 3D oral model from only ten 2D intraoral images captured from different angles, requiring no dedicated hardware; the model is trained on the public Teeth3DS dataset of 950 upper jaw samples and uses MobileNetV2 as the image encoder with Multi-head Attention for multi-view feature fusion, achieving 77.49% accuracy under nearest-neighbor matching with a distance threshold of 0.035, while predicted vertices tend to concentrate in high-density regions of the ground truth, producing uneven point distribution in the reconstructed model.
Arabian Journal of Chemistry Sep 26, 2026
This review systematically compiles progress in the design, synthesis, and biological evaluation of indole-based small molecules as antitubercular candidates targeting the mycolic acid biosynthesis pathway (MmpL3, InhA, KasA/KasB), listing per-series optimized-compound MIC values (e.g., compounds 20-22 at 0.0195 µg/mL, compound 36a at 0.024 µM, and compound 82 with MIC50 0.015 µM in the MmpL3 direction; compounds 122a at 0.39 µM and 143f at 3.99 µM in the InhA direction) alongside molecular docking results, and noting that many indole series still lack direct biochemical inhibition data such as purified-enzyme IC50/Ki and genetic validation including resistance mutations or target overexpression.
This review systematically compiles progress in the design, synthesis, and biological evaluation of indole-based small molecules as antitubercular candidates targeting the mycolic acid biosynthesis pathway (MmpL3, InhA, KasA/KasB), listing per-series optimized-compound MIC values (e.g., compounds 20-22 at 0.0195 µg/mL, compound 36a at 0.024 µM, and compound 82 with MIC50 0.015 µM in the MmpL3 direction; compounds 122a at 0.39 µM and 143f at 3.99 µM in the InhA direction) alongside molecular docking results, and noting that many indole series still lack direct biochemical inhibition data such as purified-enzyme IC50/Ki and genetic validation including resistance mutations or target overexpression.
Review maps indole derivatives targeting mycolic acid enzymes (MmpL3, InhA, KasA/B), their SAR and synthetic routes, and notes most series still lack enzymatic and genetic target validation
This review systematically compiles progress in the design, synthesis, and biological evaluation of indole-based small molecules as antitubercular candidates targeting the mycolic acid biosynthesis pathway (MmpL3, InhA, KasA/KasB), listing per-series optimized-compound MIC values (e.g., compounds 20-22 at 0.0195 µg/mL, compound 36a at 0.024 µM, and compound 82 with MIC50 0.015 µM in the MmpL3 direction; compounds 122a at 0.39 µM and 143f at 3.99 µM in the InhA direction) alongside molecular docking results, and noting that many indole series still lack direct biochemical inhibition data such as purified-enzyme IC50/Ki and genetic validation including resistance mutations or target overexpression.
This review systematically compiles progress in the design, synthesis, and biological evaluation of indole-based small molecules as antitubercular candidates targeting the mycolic acid biosynthesis pathway (MmpL3, InhA, KasA/KasB), listing per-series optimized-compound MIC values (e.g., compounds 20-22 at 0.0195 µg/mL, compound 36a at 0.024 µM, and compound 82 with MIC50 0.015 µM in the MmpL3 direction; compounds 122a at 0.39 µM and 143f at 3.99 µM in the InhA direction) alongside molecular docking results, and noting that many indole series still lack direct biochemical inhibition data such as purified-enzyme IC50/Ki and genetic validation including resistance mutations or target overexpression.
npj Digital Medicine Sep 26, 2026
This review states that, with improved computational resources and advances in artificial intelligence, current digital twins (DT) can integrate multi-omics data through hybrid mechanism- and data-driven models, enabling personalized simulation of patients, organs, and cells, which makes DT application in drug evaluation and drug repurposing discovery possible; the authors review current and emerging DT applications across the drug evaluation continuum, propose a staged development roadmap, and further highlight pivotal challenges that must be addressed to realize DT's full potential in drug evaluation, while noting that practical and regulatory issues have also emerged amid rapid development.
This review states that, with improved computational resources and advances in artificial intelligence, current digital twins (DT) can integrate multi-omics data through hybrid mechanism- and data-driven models, enabling personalized simulation of patients, organs, and cells, which makes DT application in drug evaluation and drug repurposing discovery possible; the authors review current and emerging DT applications across the drug evaluation continuum, propose a staged development roadmap, and further highlight pivotal challenges that must be addressed to realize DT's full potential in drug evaluation, while noting that practical and regulatory issues have also emerged amid rapid development.
Review proposes a staged roadmap for digital twins in drug evaluation and flags practical and regulatory challenges
This review states that, with improved computational resources and advances in artificial intelligence, current digital twins (DT) can integrate multi-omics data through hybrid mechanism- and data-driven models, enabling personalized simulation of patients, organs, and cells, which makes DT application in drug evaluation and drug repurposing discovery possible; the authors review current and emerging DT applications across the drug evaluation continuum, propose a staged development roadmap, and further highlight pivotal challenges that must be addressed to realize DT's full potential in drug evaluation, while noting that practical and regulatory issues have also emerged amid rapid development.
This review states that, with improved computational resources and advances in artificial intelligence, current digital twins (DT) can integrate multi-omics data through hybrid mechanism- and data-driven models, enabling personalized simulation of patients, organs, and cells, which makes DT application in drug evaluation and drug repurposing discovery possible; the authors review current and emerging DT applications across the drug evaluation continuum, propose a staged development roadmap, and further highlight pivotal challenges that must be addressed to realize DT's full potential in drug evaluation, while noting that practical and regulatory issues have also emerged amid rapid development.
发表出处待核验 Sep 26, 2026
This review surveys the evolution of molecular techniques in forensic identification, moving from conventional DNA profiling methods such as Restriction Fragment Length Polymorphism (RFLP) and Short Tandem Repeat (STR) analysis to advanced methodologies including mitochondrial DNA (mtDNA) analysis, Y-chromosome markers and Next-Generation Sequencing (NGS), and examines their principles, applications, advantages and limitations across crime scene investigation, human identification, kinship analysis and mass disaster victim identification, while highlighting recent advances in forensic genomics, epigenetics and microbiome-based approaches, the growing role of bioinformatics and artificial intelligence in data interpretation, and the remaining concerns of data complexity, ethical considerati
This review surveys the evolution of molecular techniques in forensic identification, moving from conventional DNA profiling methods such as Restriction Fragment Length Polymorphism (RFLP) and Short Tandem Repeat (STR) analysis to advanced methodologies including mitochondrial DNA (mtDNA) analysis, Y-chromosome markers and Next-Generation Sequencing (NGS), and examines their principles, applications, advantages and limitations across crime scene investigation, human identification, kinship analysis and mass disaster victim identification, while highlighting recent advances in forensic genomics, epigenetics and microbiome-based approaches, the growing role of bioinformatics and artificial intelligence in data interpretation, and the remaining concerns of data complexity, ethical considerati
Review traces forensic identification from RFLP and STR to mtDNA, Y-chromosome markers and next-generation sequencing, flagging data complexity, ethics and global standardization as key concerns
This review surveys the evolution of molecular techniques in forensic identification, moving from conventional DNA profiling methods such as Restriction Fragment Length Polymorphism (RFLP) and Short Tandem Repeat (STR) analysis to advanced methodologies including mitochondrial DNA (mtDNA) analysis, Y-chromosome markers and Next-Generation Sequencing (NGS), and examines their principles, applications, advantages and limitations across crime scene investigation, human identification, kinship analysis and mass disaster victim identification, while highlighting recent advances in forensic genomics, epigenetics and microbiome-based approaches, the growing role of bioinformatics and artificial intelligence in data interpretation, and the remaining concerns of data complexity, ethical considerati
This review surveys the evolution of molecular techniques in forensic identification, moving from conventional DNA profiling methods such as Restriction Fragment Length Polymorphism (RFLP) and Short Tandem Repeat (STR) analysis to advanced methodologies including mitochondrial DNA (mtDNA) analysis, Y-chromosome markers and Next-Generation Sequencing (NGS), and examines their principles, applications, advantages and limitations across crime scene investigation, human identification, kinship analysis and mass disaster victim identification, while highlighting recent advances in forensic genomics, epigenetics and microbiome-based approaches, the growing role of bioinformatics and artificial intelligence in data interpretation, and the remaining concerns of data complexity, ethical considerati
arXiv Sep 26, 2026
The work proposes TextCSP, a hierarchical text-guided brain tumor segmentation framework built on the TextBraTS baseline with three components: a text-modulated soft cascade decoder that predicts WT→TC→ET in a coarse-to-fine manner, sub-region-aware prompt tuning that uses learnable soft prompts with a LoRA-adapted BioBERT encoder to generate branch-specialized text representations, and text-semantic channel modulators that convert those representations into channel-wise refinement signals; on the TextBraTS dataset it reaches 87.0% average Dice and 4.81 mm average HD95, improving over the previous best TextBraTS by 1.7% and about 6% (0.32 mm) respectively, with consistent gains across all three sub-regions.
The work proposes TextCSP, a hierarchical text-guided brain tumor segmentation framework built on the TextBraTS baseline with three components: a text-modulated soft cascade decoder that predicts WT→TC→ET in a coarse-to-fine manner, sub-region-aware prompt tuning that uses learnable soft prompts with a LoRA-adapted BioBERT encoder to generate branch-specialized text representations, and text-semantic channel modulators that convert those representations into channel-wise refinement signals; on the TextBraTS dataset it reaches 87.0% average Dice and 4.81 mm average HD95, improving over the previous best TextBraTS by 1.7% and about 6% (0.32 mm) respectively, with consistent gains across all three sub-regions.
TextCSP combines sub-region-aware prompts with soft cascade decoding to reach 87.0% average Dice and 4.81 mm HD95 on TextBraTS
The work proposes TextCSP, a hierarchical text-guided brain tumor segmentation framework built on the TextBraTS baseline with three components: a text-modulated soft cascade decoder that predicts WT→TC→ET in a coarse-to-fine manner, sub-region-aware prompt tuning that uses learnable soft prompts with a LoRA-adapted BioBERT encoder to generate branch-specialized text representations, and text-semantic channel modulators that convert those representations into channel-wise refinement signals; on the TextBraTS dataset it reaches 87.0% average Dice and 4.81 mm average HD95, improving over the previous best TextBraTS by 1.7% and about 6% (0.32 mm) respectively, with consistent gains across all three sub-regions.
The work proposes TextCSP, a hierarchical text-guided brain tumor segmentation framework built on the TextBraTS baseline with three components: a text-modulated soft cascade decoder that predicts WT→TC→ET in a coarse-to-fine manner, sub-region-aware prompt tuning that uses learnable soft prompts with a LoRA-adapted BioBERT encoder to generate branch-specialized text representations, and text-semantic channel modulators that convert those representations into channel-wise refinement signals; on the TextBraTS dataset it reaches 87.0% average Dice and 4.81 mm average HD95, improving over the previous best TextBraTS by 1.7% and about 6% (0.32 mm) respectively, with consistent gains across all three sub-regions.
arXiv Sep 26, 2026
The work proposes SIUM, which models each modality subset's task representation as a Gaussian (mean carrying task information, variance measuring uncertainty from missing evidence), aligns subset means toward a full-modality anchor via an uncertainty-aware alignment loss that scales variance with their discrepancy, and adds an uncertainty ordering loss keeping subset variance above its supersets, achieving better segmentation than RFNet, mmFormer, M3AE, and DC-Seg across diverse missing-modality configurations on BraTS 2018 and 2020.
The work proposes SIUM, which models each modality subset's task representation as a Gaussian (mean carrying task information, variance measuring uncertainty from missing evidence), aligns subset means toward a full-modality anchor via an uncertainty-aware alignment loss that scales variance with their discrepancy, and adds an uncertainty ordering loss keeping subset variance above its supersets, achieving better segmentation than RFNet, mmFormer, M3AE, and DC-Seg across diverse missing-modality configurations on BraTS 2018 and 2020.
Treating missing modalities as uncertainty: SIUM reaches average Dice of 88.07/87.10/62.52 on BraTS 2018/2020 under missing-modality settings
The work proposes SIUM, which models each modality subset's task representation as a Gaussian (mean carrying task information, variance measuring uncertainty from missing evidence), aligns subset means toward a full-modality anchor via an uncertainty-aware alignment loss that scales variance with their discrepancy, and adds an uncertainty ordering loss keeping subset variance above its supersets, achieving better segmentation than RFNet, mmFormer, M3AE, and DC-Seg across diverse missing-modality configurations on BraTS 2018 and 2020.
The work proposes SIUM, which models each modality subset's task representation as a Gaussian (mean carrying task information, variance measuring uncertainty from missing evidence), aligns subset means toward a full-modality anchor via an uncertainty-aware alignment loss that scales variance with their discrepancy, and adds an uncertainty ordering loss keeping subset variance above its supersets, achieving better segmentation than RFNet, mmFormer, M3AE, and DC-Seg across diverse missing-modality configurations on BraTS 2018 and 2020.
arXiv Sep 26, 2026
The work proposes DINO-3DRA, a dual-path framework that injects frozen 2D vision foundation model DINOv3-Small features into a trainable 3D U-Net backbone through Room-Lite spatial mixing and calibrated residual fusion, achieving state-of-the-art aneurysm segmentation on multi-centre 3D rotational angiography (3DRA) @neurIST data (233 patients, four institutions) with Dice 0.758, HD95 2.75 mm and a 13% gain over nnU-Net using only 5.72M trainable parameters; ablations attribute the gains to structured cross-dimensional transfer rather than loss design, and without fine-tuning on CADA (n=45) and SHINY-ICARUS (n=30) it reduces Dice<0.5 failures from 11.1% and 3.3% to 0%.
The work proposes DINO-3DRA, a dual-path framework that injects frozen 2D vision foundation model DINOv3-Small features into a trainable 3D U-Net backbone through Room-Lite spatial mixing and calibrated residual fusion, achieving state-of-the-art aneurysm segmentation on multi-centre 3D rotational angiography (3DRA) @neurIST data (233 patients, four institutions) with Dice 0.758, HD95 2.75 mm and a 13% gain over nnU-Net using only 5.72M trainable parameters; ablations attribute the gains to structured cross-dimensional transfer rather than loss design, and without fine-tuning on CADA (n=45) and SHINY-ICARUS (n=30) it reduces Dice<0.5 failures from 11.1% and 3.3% to 0%.
Frozen DINOv3 features injected into a 3D U-Net via Room-Lite mixing and calibrated fusion reach Dice 0.758 for 3DRA aneurysm segmentation and remove all cross-dataset failures
The work proposes DINO-3DRA, a dual-path framework that injects frozen 2D vision foundation model DINOv3-Small features into a trainable 3D U-Net backbone through Room-Lite spatial mixing and calibrated residual fusion, achieving state-of-the-art aneurysm segmentation on multi-centre 3D rotational angiography (3DRA) @neurIST data (233 patients, four institutions) with Dice 0.758, HD95 2.75 mm and a 13% gain over nnU-Net using only 5.72M trainable parameters; ablations attribute the gains to structured cross-dimensional transfer rather than loss design, and without fine-tuning on CADA (n=45) and SHINY-ICARUS (n=30) it reduces Dice<0.5 failures from 11.1% and 3.3% to 0%.
The work proposes DINO-3DRA, a dual-path framework that injects frozen 2D vision foundation model DINOv3-Small features into a trainable 3D U-Net backbone through Room-Lite spatial mixing and calibrated residual fusion, achieving state-of-the-art aneurysm segmentation on multi-centre 3D rotational angiography (3DRA) @neurIST data (233 patients, four institutions) with Dice 0.758, HD95 2.75 mm and a 13% gain over nnU-Net using only 5.72M trainable parameters; ablations attribute the gains to structured cross-dimensional transfer rather than loss design, and without fine-tuning on CADA (n=45) and SHINY-ICARUS (n=30) it reduces Dice<0.5 failures from 11.1% and 3.3% to 0%.
arXiv Sep 26, 2026
The work presents SegDINO, which uses a frozen DINOv3-S encoder to collect intermediate features from layers 3, 6, 9, and 12, reorganizes same-resolution tokens into a pseudo multi-scale pyramid via Token Pyramid Adaptation (TPA), and applies Scale-Aware Decoding (SAD) for intra-scale refinement and top-down inter-scale propagation, alongside a new PanCT dataset of 284 pancreatic cancer patient CT scans; on PanCT and the public TN3K, Kvasir-SEG, and ISIC benchmarks, SegDINO outperforms U-Net, SegNet, R2U-Net, Attention U-Net, TransUNet, U-NeXt, and U-KAN in both DSC and HD95, with 27.68M total parameters and 51 FPS inference.
The work presents SegDINO, which uses a frozen DINOv3-S encoder to collect intermediate features from layers 3, 6, 9, and 12, reorganizes same-resolution tokens into a pseudo multi-scale pyramid via Token Pyramid Adaptation (TPA), and applies Scale-Aware Decoding (SAD) for intra-scale refinement and top-down inter-scale propagation, alongside a new PanCT dataset of 284 pancreatic cancer patient CT scans; on PanCT and the public TN3K, Kvasir-SEG, and ISIC benchmarks, SegDINO outperforms U-Net, SegNet, R2U-Net, Attention U-Net, TransUNet, U-NeXt, and U-KAN in both DSC and HD95, with 27.68M total parameters and 51 FPS inference.
SegDINO reshapes DINOv3 with lightweight scale modeling, reaching top segmentation accuracy across four datasets at 27.68M parameters and 51 FPS
The work presents SegDINO, which uses a frozen DINOv3-S encoder to collect intermediate features from layers 3, 6, 9, and 12, reorganizes same-resolution tokens into a pseudo multi-scale pyramid via Token Pyramid Adaptation (TPA), and applies Scale-Aware Decoding (SAD) for intra-scale refinement and top-down inter-scale propagation, alongside a new PanCT dataset of 284 pancreatic cancer patient CT scans; on PanCT and the public TN3K, Kvasir-SEG, and ISIC benchmarks, SegDINO outperforms U-Net, SegNet, R2U-Net, Attention U-Net, TransUNet, U-NeXt, and U-KAN in both DSC and HD95, with 27.68M total parameters and 51 FPS inference.
The work presents SegDINO, which uses a frozen DINOv3-S encoder to collect intermediate features from layers 3, 6, 9, and 12, reorganizes same-resolution tokens into a pseudo multi-scale pyramid via Token Pyramid Adaptation (TPA), and applies Scale-Aware Decoding (SAD) for intra-scale refinement and top-down inter-scale propagation, alongside a new PanCT dataset of 284 pancreatic cancer patient CT scans; on PanCT and the public TN3K, Kvasir-SEG, and ISIC benchmarks, SegDINO outperforms U-Net, SegNet, R2U-Net, Attention U-Net, TransUNet, U-NeXt, and U-KAN in both DSC and HD95, with 27.68M total parameters and 51 FPS inference.
arXiv Sep 26, 2026
The work formalizes few-shot multi-rater medical image segmentation and proposes a prototype-centric personalization framework: a consensus mask and consensus prototype are averaged from multi-rater masks, each rater prototype's deviation from the consensus prototype serves as the attention key, and a shared self-attention module calibrates the concatenated rater prototypes, combined with a calibration loss, pseudo-style supervision synthesized from superpixel pseudo labels via random structured boundary transformations, and two-stage training; on CURVAS abdominal CT (kidney, pancreas, liver; three experts; 20 training and 65 test scans) and QUBIQ brain-growth MRI (one class, seven raters; 34 training and 5 test scans), evaluated by per-rater Dice, the method improves consistently over pro
The work formalizes few-shot multi-rater medical image segmentation and proposes a prototype-centric personalization framework: a consensus mask and consensus prototype are averaged from multi-rater masks, each rater prototype's deviation from the consensus prototype serves as the attention key, and a shared self-attention module calibrates the concatenated rater prototypes, combined with a calibration loss, pseudo-style supervision synthesized from superpixel pseudo labels via random structured boundary transformations, and two-stage training; on CURVAS abdominal CT (kidney, pancreas, liver; three experts; 20 training and 65 test scans) and QUBIQ brain-growth MRI (one class, seven raters; 34 training and 5 test scans), evaluated by per-rater Dice, the method improves consistently over pro
Calibrating rater differences in prototype space with attention lets few-shot medical segmentation emit per-rater predictions and raise Dice on CURVAS and QUBIQ
The work formalizes few-shot multi-rater medical image segmentation and proposes a prototype-centric personalization framework: a consensus mask and consensus prototype are averaged from multi-rater masks, each rater prototype's deviation from the consensus prototype serves as the attention key, and a shared self-attention module calibrates the concatenated rater prototypes, combined with a calibration loss, pseudo-style supervision synthesized from superpixel pseudo labels via random structured boundary transformations, and two-stage training; on CURVAS abdominal CT (kidney, pancreas, liver; three experts; 20 training and 65 test scans) and QUBIQ brain-growth MRI (one class, seven raters; 34 training and 5 test scans), evaluated by per-rater Dice, the method improves consistently over pro
The work formalizes few-shot multi-rater medical image segmentation and proposes a prototype-centric personalization framework: a consensus mask and consensus prototype are averaged from multi-rater masks, each rater prototype's deviation from the consensus prototype serves as the attention key, and a shared self-attention module calibrates the concatenated rater prototypes, combined with a calibration loss, pseudo-style supervision synthesized from superpixel pseudo labels via random structured boundary transformations, and two-stage training; on CURVAS abdominal CT (kidney, pancreas, liver; three experts; 20 training and 65 test scans) and QUBIQ brain-growth MRI (one class, seven raters; 34 training and 5 test scans), evaluated by per-rater Dice, the method improves consistently over pro
arXiv Sep 26, 2026
The work proposes BiM-GeoAttn-Net, which cascades a Bidirectional Depth Mamba (BiM) and a Geometry-Aware Vessel Attention (GeoAttn) module at the nnU-Net bottleneck, and on 71 multi-source Stanford Type-B aortic dissection CTA cases performs binary segmentation of the vessel foreground (true plus false lumen), achieving Dice 93.35%, IoU 87.53%, Recall 94.85%, Precision 92.04%, and HD95 12.36 mm, outperforming Attention U-Net, nnU-Net, Swin-UNet, SegFormer3D, and Mamba-UNet on overlap metrics while keeping boundary accuracy and computational cost competitive.
The work proposes BiM-GeoAttn-Net, which cascades a Bidirectional Depth Mamba (BiM) and a Geometry-Aware Vessel Attention (GeoAttn) module at the nnU-Net bottleneck, and on 71 multi-source Stanford Type-B aortic dissection CTA cases performs binary segmentation of the vessel foreground (true plus false lumen), achieving Dice 93.35%, IoU 87.53%, Recall 94.85%, Precision 92.04%, and HD95 12.36 mm, outperforming Attention U-Net, nnU-Net, Swin-UNet, SegFormer3D, and Mamba-UNet on overlap metrics while keeping boundary accuracy and computational cost competitive.
BiM-GeoAttn-Net reaches 93.35% Dice on 71 aortic dissection CTA cases, beating CNN, Transformer, and SSM baselines
The work proposes BiM-GeoAttn-Net, which cascades a Bidirectional Depth Mamba (BiM) and a Geometry-Aware Vessel Attention (GeoAttn) module at the nnU-Net bottleneck, and on 71 multi-source Stanford Type-B aortic dissection CTA cases performs binary segmentation of the vessel foreground (true plus false lumen), achieving Dice 93.35%, IoU 87.53%, Recall 94.85%, Precision 92.04%, and HD95 12.36 mm, outperforming Attention U-Net, nnU-Net, Swin-UNet, SegFormer3D, and Mamba-UNet on overlap metrics while keeping boundary accuracy and computational cost competitive.
The work proposes BiM-GeoAttn-Net, which cascades a Bidirectional Depth Mamba (BiM) and a Geometry-Aware Vessel Attention (GeoAttn) module at the nnU-Net bottleneck, and on 71 multi-source Stanford Type-B aortic dissection CTA cases performs binary segmentation of the vessel foreground (true plus false lumen), achieving Dice 93.35%, IoU 87.53%, Recall 94.85%, Precision 92.04%, and HD95 12.36 mm, outperforming Attention U-Net, nnU-Net, Swin-UNet, SegFormer3D, and Mamba-UNet on overlap metrics while keeping boundary accuracy and computational cost competitive.

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