Topologically faithful multi-class segmentation in medical images
Topological accuracy in medical image segmentation is a highly important property for
downstream applications such as network analysis and flow modeling in vessels or cell …
downstream applications such as network analysis and flow modeling in vessels or cell …
Non-Invasive Tools in Perioperative Stroke Risk Assessment for Asymptomatic Carotid Artery Stenosis with a Focus on the Circle of Willis
B Lengyel, R Magyar-Stang, H Pál… - Journal of Clinical …, 2024 - mdpi.com
This review aims to explore advancements in perioperative ischemic stroke risk estimation
for asymptomatic patients with significant carotid artery stenosis, focusing on Circle of Willis …
for asymptomatic patients with significant carotid artery stenosis, focusing on Circle of Willis …
Centerline Boundary Dice Loss for Vascular Segmentation
Vascular segmentation in medical imaging plays a crucial role in analysing morphological
and functional assessments. Traditional methods, like the centerline Dice (clDice) loss …
and functional assessments. Traditional methods, like the centerline Dice (clDice) loss …
Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis
Although existing medical image segmentation methods provide impressive pixel-wise
accuracy, they often neglect topological correctness, making their segmentations unusable …
accuracy, they often neglect topological correctness, making their segmentations unusable …
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. Standard deep learning-based segmentation …
cracks, is a crucial task in computer vision. Standard deep learning-based segmentation …
Topograph: An efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation
Topological correctness plays a critical role in many image segmentation tasks, yet most
networks are trained using pixel-wise loss functions, such as Dice, neglecting topological …
networks are trained using pixel-wise loss functions, such as Dice, neglecting topological …
ISLES'24: Improving final infarct prediction in ischemic stroke using multimodal imaging and clinical data
Accurate estimation of core (irreversibly damaged tissue) and penumbra (salvageable
tissue) volumes is essential for ischemic stroke treatment decisions. Perfusion CT, the …
tissue) volumes is essential for ischemic stroke treatment decisions. Perfusion CT, the …
3D Vessel Graph Generation Using Denoising Diffusion
C Prabhakar, S Shit, F Musio, K Yang… - … Conference on Medical …, 2024 - Springer
Blood vessel networks, represented as 3D graphs, help predict disease biomarkers,
simulate blood flow, and aid in synthetic image generation, relevant in both clinical and pre …
simulate blood flow, and aid in synthetic image generation, relevant in both clinical and pre …
Guidelines for cerebrovascular segmentation: Managing imperfect annotations in the context of semi-supervised learning
Segmentation in medical imaging is an essential and often preliminary task in the image
processing chain, driving numerous efforts towards the design of robust segmentation …
processing chain, driving numerous efforts towards the design of robust segmentation …
Pitfalls of topology-aware image segmentation
Topological correctness, ie, the preservation of structural integrity and specific
characteristics of shape, is a fundamental requirement for medical imaging tasks, such as …
characteristics of shape, is a fundamental requirement for medical imaging tasks, such as …