Artif Intell Med 2018 Jun 19;88:1-13. Epub 2018 Apr 19.
Institute of Applied Computer Science, Lodz University of Technology, 18/22 Stefanowskiego Str., 90-924 Lodz, Poland. Electronic address:
Download full-text PDF
J Med Imaging (Bellingham) 2018 Apr 17;5(2):021208. Epub 2018 Jan 17.
Emory University School of Medicine, Department of Radiology and Imaging Sciences, Atlanta, Georgia, United States.
Automatic segmentation of the prostate on magnetic resonance images (MRI) has many applications in prostate cancer diagnosis and therapy. We proposed a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage, which uses prostate MRI and the corresponding ground truths as inputs. Read More
Comput Med Imaging Graph 2017 Jan 9;55:13-27. Epub 2016 Aug 9.
Bialystok University of Technology, Faculty of Computer Science, Bialystok, Poland.
The corneal endothelium state is verified on the basis of an in vivo specular microscope image from which the shape and density of cells are exploited for data description. Due to the relatively low image quality resulting from a high magnification of the living, non-stained tissue, both manual and automatic analysis of the data is a challenging task. Although, many automatic or semi-automatic solutions have already been introduced, all of them are prone to inaccuracy. Read More
Comput Methods Programs Biomed 2018 Jul 22;160:11-23. Epub 2018 Mar 22.
Manchester Royal Eye Hospital, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK.
Background And Objective: Corneal endothelial cell abnormalities may be associated with a number of corneal and systemic diseases. Damage to the endothelial cells can significantly affect corneal transparency by altering hydration of the corneal stroma, which can lead to irreversible endothelial cell pathology requiring corneal transplantation. To date, quantitative analysis of endothelial cell abnormalities has been manually performed by ophthalmologists using time consuming and highly subjective semi-automatic tools, which require an operator interaction. Read More
IEEE J Biomed Health Inform 2018 Aug 14. Epub 2018 Aug 14.
In this paper, we present a novel convolutional neural network (CNN) architecture to segment images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed model is an extension of the U-net that embeds a cardiac shape prior and involves a loss function tailored to the cardiac anatomy. Our system takes as input raw MR images, requires no manual preprocessing or image cropping and is trained to segment the endocardium and epicardium of the left ventricle, the endocardium of the right ventricle, as well as the center of the left ventricle. Read More