Track topics on Twitter Track topics that are important to you
While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images.
This article was published in the following journal.
Name: Bioinformatics (Oxford, England)
Automatic segmentation of organs-at-risk (OARs) is a key step in radiation treatment planning to reduce human efforts and bias. Deep convolutional neural networks (DCNN) have shown great success in ma...
Fully convolutional neural networks have been shown to perform well for automated skin lesion segmentation on digital dermatoscopic images. Our concept is that transferring encoder weights from a netw...
To investigate the use and efficiency of 3-D deep learning, fully convolutional networks (DFCN) for simultaneous tumor co-segmentation on dual-modality non-small cell lung cancer (NSCLC) PET-CT images...
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning archit...
To develop an automated vessel wall segmentation method using convolutional neural networks (CNN) to facilitate the quantification on magnetic resonance (MR) vessel wall images of patients with intrac...
Using an automatic software tool, Pixyl.Neuro, to conduct a retrospective analysis (detection of lesions + segmentation of images + tracking over time) of cerebral MRI images acquired duri...
Limited pancreatic resections are increasingly performed, but the rate of postoperative fistula is higher than after classical resections. Pancreatic segmentation, anatomically and radiolo...
Linked color imaging (LCI)，a new endoscopy modality, creates clear and bright images by using short wavelength narrow band laser light. LCI can make red area appear redder and white areas ...
Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low risk of harbouring cancer. Current practice is to send all these polyps for histopathological as...
This is a pragmatic, non-inferiority, randomized controlled trial comparing the effectiveness of two methods (crowdsourcing versus social marketing) for creating one-minute videos promotin...
The process of generating three-dimensional images by electronic, photographic, or other methods. For example, three-dimensional images can be generated by assembling multiple tomographic images with the aid of a computer, while photographic 3-D images (HOLOGRAPHY) can be made by exposing film to the interference pattern created when two laser light sources shine on an object.
Combination or superimposition of two images for demonstrating differences between them (e.g., radiograph with contrast vs. one without, radionuclide images using different radionuclides, radiograph vs. radionuclide image) and in the preparation of audiovisual materials (e.g., offsetting identical images, coloring of vessels in angiograms).
A non-invasive method that uses a CT scanner for capturing images of blood vessels and tissues. A CONTRAST MATERIAL is injected, which helps produce detailed images that aid in diagnosing VASCULAR DISEASES.
Social media model for enabling public involvement and recruitment in participation. Use of social media to collect feedback and recruit volunteer subjects.
Imaging methods that result in sharp images of objects located on a chosen plane and blurred images located above or below the plane.