Compare deep learning cell segmentation tools Cellpose and StarDist: how each works, how they differ by imaging type, and ...
Artificial intelligence (AI) in research histopathology is turning whole-slide images of preclinical tissue into structured, quantitative data rather than a pathologist's subjective impression alone.
A new deep-learning model improved surgeons’ recognition of pelvic anatomy in video-based PLND tests, though live surgical ...
The field of orofacial medicine increasingly recognizes the temporomandibular joint (TMJ) as a complex anatomical and functional unit whose disorders can ...
Abstract: Multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Corresponding multimodal image analysis and ensemble learning schemes have ...
Modern medical imaging increasingly relies on artificial intelligence to support detection, diagnosis, and prognostic ...
In this interview, AZoLife Sciences speaks with Boyd Butler, a microscopy and high-content screening expert at Molecular ...
Abstract: This article provides a comprehensive review of deep learning-based blood vessel segmentation of the brain. Cerebrovascular disease develops when blood arteries in the brain are compromised, ...
Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...
Stroke is the second leading cause of death globally. Ischemic stroke, strongly linked to atherosclerotic plaques, requires accurate plaque and vessel wall segmentation and quantification for ...
Researchers from Science Tokyo develop a Multi-scale Hessian-enhanced Patch-based Neural Network Model for Segmentation of Liver Tumor from CT Scans. Liver cancer is the sixth most common cancer ...
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