AIMC Topic: Image Processing, Computer-Assisted

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CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network.

Korean journal of radiology
OBJECTIVE: The aim of our study was to develop and validate a convolutional neural network (CNN) architecture to convert CT images reconstructed with one kernel to images with different reconstruction kernels without using a sinogram.

Deep Learning for Chondrocyte Identification in Automated Histological Analysis of Articular Cartilage.

The Iowa orthopaedic journal
BACKGROUND: Histology-based methods are commonly used in osteoarthritis (OA) research because they provide detailed information about cartilage health at the cellular and tissue level. Computer-based cartilage scoring systems have previously been dev...

An Intensity Variation Pattern Analysis Based Machine Learning Classifier for MRI Brain Tumor Detection.

Current medical imaging reviews
BACKGROUND: An accurate detection of tumor from the Magnetic Resonance Images (MRIs) is a critical and demanding task in medical image processing, due to the varying shape and structure of brain. So, different segmentation approaches such as manual, ...

Deep Learning for Alzheimer's Disease Classification using Texture Features.

Current medical imaging reviews
BACKGROUND: We propose a classification method for Alzheimer's disease (AD) based on the texture of the hippocampus, which is the organ that is most affected by the onset of AD.

The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods.

Technology in cancer research & treatment
Radiotherapy is the main treatment strategy for nasopharyngeal carcinoma. A major factor affecting radiotherapy outcome is the accuracy of target delineation. Target delineation is time-consuming, and the results can vary depending on the experience ...

Machine Learning: Advanced Image Segmentation Using ilastik.

Methods in molecular biology (Clifton, N.J.)
Segmentation is one of the most ubiquitous problems in biological image analysis. Here we present a machine learning-based solution to it as implemented in the open source ilastik toolkit. We give a broad description of the underlying theory and demo...

A Cell Segmentation/Tracking Tool Based on Machine Learning.

Methods in molecular biology (Clifton, N.J.)
The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we present a detailed protocol for obtaining quality time-lapse movies and introduce a method to identify (se...

Automatic prostate segmentation based on fusion between deep network and variational methods.

Journal of X-ray science and technology
BACKGROUND: Segmentation of prostate from magnetic resonance images (MRI) is a critical process for guiding prostate puncture and biopsy. Currently, the best results are obtained by Convolutional Neural Network (CNN). However, challenges still exist ...

New one-step model of breast tumor locating based on deep learning.

Journal of X-ray science and technology
BACKGROUND: Breast cancer has the highest cancer prevalence rate among the women worldwide. Early detection of breast cancer is crucial for successful treatment and reducing cancer mortality rate. However, tumor detection of breast ultrasound (US) im...

A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI.

Technology in cancer research & treatment
PURPOSE: In prostate focal therapy, it is important to accurately localize malignant lesions in order to increase biological effect of the tumor region while achieving a reduction in dose to noncancerous tissue. In this work, we proposed a transfer l...