Results 51 to 60 of about 16,791,752 (293)
A review of medical ocular image segmentation
Virtual Reality & Intelligent HardwareDeep learning has been extensively applied to medical image segmentation, resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U-Net in 2015.
Lai WEI, Menghan HU
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BRAIN TUMOUR SEGMENTATION STRATEGIES UTILIZING MEAN SHIFT CLUSTERING AND CONTENT BASED ACTIVE CONTOUR SEGMENTATION [PDF]
ICTACT Journal on Image and Video Processing, 2019 This paper proposes an automatic brain tumor segmentation using Mean shift clustering and content based active contour segmentation. In diagnosis of the disease medical imaging has more advantages. Many people suffer from brain tumor, it is a serious and
D M Mahalakshmi, S Sumathi
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FEBS Letters, EarlyView.Enteropathogenic E. coli (EPEC) infects the human intestinal epithelium, resulting in severe illness and diarrhoea. In this study, we compared the infection of cancer‐derived cell lines with human organoid‐derived models of the small intestine. We observed a delayed in attachment, inflammation and cell death on primary cells, indicating that host ...
Mastura Neyazi +5 more
wiley +1 more source
A comparative evaluation of interactive segmentation algorithms [PDF]
, 2010 In this paper we present a comparative evaluation of four popular interactive segmentation algorithms. The evaluation was carried out as a series of user-experiments, in which participants were tasked with extracting 100 objects from a common dataset: 25
McGuinness, Kevin +2 more
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FEBS Letters, EarlyView.Biomolecular condensates formed by fused in sarcoma (FUS) are dissolved by high ATP concentrations yet persist in cells. Using a reconstituted system, we demonstrate that valosin‐containing protein (VCP), an AAA+ ATPase, counteracts ATP‐driven dissolution of FUS condensates through its D2 ATPase activity.
Hitomi Kimura +2 more
wiley +1 more source
Segmenting medical images with limited data
Neural NetworksWhile computer vision has proven valuable for medical image segmentation, its application faces challenges such as limited dataset sizes and the complexity of effectively leveraging unlabeled images. To address these challenges, we present a novel semi-supervised, consistency-based approach termed the data-efficient medical segmenter (DEMS).
Zhaoshan Liu +3 more
openaire +5 more sources
FEBS Letters, EarlyView.We present robust protocols for the preparation of supported lipid bilayers (SLBs) incorporating either Salmonella smooth LPS or outer membrane vesicles (OMVs). We use a combination of quartz crystal microbalance with dissipation (QCM‐D) and fluorescence microscopy to both characterize the SLBs of various compositions and to probe their interactions ...
Hudson P. Pace +6 more
wiley +1 more source
Modelling stem cell differentiation related processes—A practical overview for biologists
FEBS Letters, EarlyView.Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source
Image segmentation evaluation using an integrated framework [PDF]
, 2007 In this paper we present a general framework we have developed for running and evaluating automatic image and video segmentation algorithms. This framework was designed to allow effortless integration of existing and forthcoming image segmentation ...
McGuinness, Kevin +3 more
core +2 more sources
Interactive Segmentation for Medical Images Using Spatial Modeling Mamba
InformationInteractive segmentation methods utilize user-provided positive and negative clicks to guide the model in accurately segmenting target objects. Compared to fully automatic medical image segmentation, these methods can achieve higher segmentation accuracy
Yuxin Tang +3 more
doaj +1 more source