AIMC Topic: Image Processing, Computer-Assisted

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Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification.

IEEE transactions on medical imaging
Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the de...

Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks.

Journal of neurointerventional surgery
BACKGROUND AND PURPOSE: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in cli...

Mitochondria Segmentation From EM Images via Hierarchical Structured Contextual Forest.

IEEE journal of biomedical and health informatics
Delineation of mitochondria from electron microscopy (EM) images is crucial to investigate its morphology and distribution, which are directly linked to neural dysfunction. However, it is a challenging task due to the varied appearances, sizes and sh...

Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks.

Artificial intelligence in medicine
Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated to be a valid complementary diagnostic tool for early detection and diagnosis of breast cancer. However, without a CAD (Computer Aided Detection) system, manual...

Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images.

Medical image analysis
The tracking of the knee femoral condyle cartilage during ultrasound-guided minimally invasive procedures is important to avoid damaging this structure during such interventions. In this study, we propose a new deep learning method to track, accurate...

Fully automatic estimation of pelvic sagittal inclination from anterior-posterior radiography image using deep learning framework.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Malposition of the acetabular component causes dislocation and prosthetic impingement after Total Hip Arthroplasty (THA), which significantly affects the postoperative quality of life and implant longevity. The position of t...

Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet.

Proceedings of the National Academy of Sciences of the United States of America
Background fluorescence, especially when it exhibits undesired spatial features, is a primary factor for reduced image quality in optical microscopy. Structured background is particularly detrimental when analyzing single-molecule images for 3-dimens...

Study on the Classification Performance of Underwater Sonar Image Classification Based on Convolutional Neural Networks for Detecting a Submerged Human Body.

Sensors (Basel, Switzerland)
Auto-detecting a submerged human body underwater is very challenging with the absolute necessity to a diver or a submersible. For the vision sensor, the water turbidity and limited light condition make it difficult to take clear images. For this reas...

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning.

NMR in biomedicine
Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. Howev...

Ultrasonic liver steatosis quantification by a learning-based acoustic model from a novel shear wave sequence.

Biomedical engineering online
BACKGROUND: An efficient and accurate approach to quantify the steatosis extent of liver is important for clinical practice. For the purpose, we propose a specific designed ultrasound shear wave sequence to estimate ultrasonic and shear wave physical...