AIMC Topic: Image Processing, Computer-Assisted

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An end-to-end approach to segmentation in medical images with CNN and posterior-CRF.

Medical image analysis
Conditional Random Fields (CRFs) are often used to improve the output of an initial segmentation model, such as a convolutional neural network (CNN). Conventional CRF approaches in medical imaging use manually defined features, such as intensity to i...

DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation.

Medical image analysis
Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation condit...

Extending Camera's Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising.

Sensors (Basel, Switzerland)
Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper,...

Magnetic Resonance Imaging Image Feature Analysis Algorithm under Convolutional Neural Network in the Diagnosis and Risk Stratification of Prostate Cancer.

Journal of healthcare engineering
This work aimed to explore the accuracy of magnetic resonance imaging (MRI) images based on the convolutional neural network (CNN) algorithm in the diagnosis of prostate cancer patients and tumor risk grading. A total of 89 patients with prostate can...

DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning.

Magnetic resonance in medicine
PURPOSE: To improve the estimation of coil sensitivity functions from limited auto-calibration signals (ACS) in SENSE-based reconstruction for brain imaging.

ResGANet: Residual group attention network for medical image classification and segmentation.

Medical image analysis
In recent years, deep learning technology has shown superior performance in different fields of medical image analysis. Some deep learning architectures have been proposed and used for computational pathology classification, segmentation, and detecti...

Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Delayed Enhancement cardiac MRI (DE-MRI) has become indispensable for the diagnosis of myocardial diseases. However, to quantify the disease severity, doctors need time to manually annotate the scar and myocardium. To address this issue, in this pape...

Deep learning enables genetic analysis of the human thoracic aorta.

Nature genetics
Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance i...

DCNet: Densely Connected Deep Convolutional Encoder-Decoder Network for Nasopharyngeal Carcinoma Segmentation.

Sensors (Basel, Switzerland)
Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor volume (GTV). However, the large-scale variation in tumor volume is intractab...