Computed tomography

UNIVERSAL MULTI-MODAL DEEP NETWORK FOR CLASSIFICATION AND SEGMENTATION OF MEDICAL IMAGES

Universal multi-modal deep network for classification and segmentation of medical images

Abstract Medical image processing algorithms have traditionally focused on a specific problem or disease per modality. This approach has continued with the wide-spread adoption of deep learning in the last […]

Universal multi-modal deep network for classification and segmentation of medical images Read More »

DEEP LEARNING BASED SEGMENTATION OF BODY PARTS IN CT LOCALIZERS ANDAPPLICATION TO SCAN PLANNING

Deep Learning Based Segmentation of Body Parts in CT Localizers and Application to Scan Planning

Abstract in this paper, we propose a deep learning approach for the segmentation of body parts in computer tomography (CT) localizer images. Such images pose dif[1]culties in the automatic image

Deep Learning Based Segmentation of Body Parts in CT Localizers and Application to Scan Planning Read More »

A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis

A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis

Abstract COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment

A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis Read More »

High-Resolution Encoder–Decoder Networks for Low-Contrast Medical Image Segmentation

High-Resolution Encoder–Decoder Networks for Low-Contrast Medical Image Segmentation

Abstract Automatic image segmentation is an essential step for many medical image analysis applications, include computer-aided radiation therapy, disease diagnosis, and treatment effect evaluation. One of the major challenges for

High-Resolution Encoder–Decoder Networks for Low-Contrast Medical Image Segmentation Read More »

High-Resolution Encoder-Decoder Networks for Low-Contrast Medical Image Segmentation

High-Resolution Encoder-Decoder Networks for Low-Contrast Medical Image Segmentation

Abstract Automatic image segmentation is an essential step for many medical image analysis applications, include computer-aided radiation therapy, disease diagnosis, and treatment effect evaluation. One of the major challenges for

High-Resolution Encoder-Decoder Networks for Low-Contrast Medical Image Segmentation Read More »

Two-Stage Convolutional Neural Network(CNN) for Medical Noise Removal via Image Decomposition

Two-Stage Convolutional Neural Network (CNN) for Medical Noise Removal via Image Decomposition

Abstract Most of the existing medical image denoising methods focus on estimating either the image or the residual noise. Moreover, they are usually designed for one specific noise with a

Two-Stage Convolutional Neural Network (CNN) for Medical Noise Removal via Image Decomposition Read More »