C&EN

Machine-Learning-Based Automated Schlemm’s Canal Volumetric Segmentation for Optical Coherence Tomography

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Numerous challenges, including the variable anatomy of SC, ...
Frontiers

FLA-UNet: feature-location attention U-Net for foveal avascular zone segmentation in OCTA images

1 School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, China 2 School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China ...
IEEE

Image Segmentation Metrics in Skin Lesion: Accuracy, Sensitivity, Specificity, Dice Coefficient, Jaccard Index, and Matthews Correlation Coefficient

Abstract: One of the main problems in skin lesion detection is image segmentation. This method is essential not only for image processing-based but also for machine learning-based skin lesion ...
Frontiers

Automatic segmentation and quantitative analysis of brain CT volume in 2-year-olds using deep learning model

Objective: Our research aims to develop an automated method for segmenting brain CT images in healthy 2-year-old children using the ResU-Net deep learning model. Building on this model, we aim to ...
Scientific Research Publishing

Wang, J.J., et al. (2017) Associations among Image Assessments as Cost Functions in Linear Decomposition: MSE, SSIM, and Correlation Coefficient. arXiv: 1708.01541。

ABSTRACT: In this paper, we study edge detection or segmentation, which is recognized as a rudiment innovation as it can evaluate sharpness and analyze object boundaries. That’s the reason it has been ...
emjreviews.com

Deep-Learning Model for Lung Tumour Detection and Segmentation

NEW CUTTING-edge deep-learning model using lung CT imaging has shown significant potential for accurately identifying and segmenting lung tumours, according to researchers based in the USA. The ...