The analysis of leukocyte recruitment in intravital video microscopy (IVM) is essential to the understanding of inflammatory processes. However, because IVM images often present a large variety of visual characteristics, it is hard for an expert huma...
Three-dimensional facial images are becoming more and more widespread. As such images provide more information about facial morphology than 2D imagery, they show great promise for use in future forensic applications, including age estimation and veri...
Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis ch...
International journal of computer assisted radiology and surgery
Nov 21, 2020
PURPOSE: We investigated the parameter configuration in the automatic liver and tumor segmentation using a convolutional neural network based on 2.5D model. The implementation of 2.5D model shows promising results since it allows the network to have ...
Protein science : a publication of the Protein Society
Nov 20, 2020
For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open-source i...
IEEE transactions on bio-medical engineering
Nov 20, 2020
OBJECTIVE: According to the European Reference Organization for Quality Assured Breast Cancer Screening and Diagnostic Services (EUREF) image quality in mammography is assessed by recording and analyzing a set of images of the CDMAM phantom. The EURE...
Deep learning (DL)-based auto-segmentation has the potential for accurate organ delineation in radiotherapy applications but requires large amounts of clean labeled data to train a robust model. However, annotating medical images is extremely time-co...
International journal of medical informatics
Nov 19, 2020
BACKGROUND: Reliable localization and tracking of the eye region in the pediatric hospital environment is a significant challenge for clinical decision support and patient monitoring applications. Existing work in eye localization achieves high perfo...
Convolutional Neural Networks (CNNs) work very well for supervised learning problems when the training dataset is representative of the variations expected to be encountered at test time. In medical image segmentation, this premise is violated when t...
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