AIMC Topic: Imaging, Three-Dimensional

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Learning tree-structured representation for 3D coronary artery segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Extensive research has been devoted to the segmentation of the coronary artery. However, owing to its complex anatomical structure, it is extremely challenging to automatically segment the coronary artery from 3D coronary computed tomography angiogra...

Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound.

Academic radiology
Recent advances in fetal imaging open the door to enhanced detection of fetal disorders and computer-assisted surgical planning. However, precise segmentation of womb's tissues is challenging due to motion artifacts caused by fetal movements and mate...

An automated 3D modeling pipeline for constructing 3D models of MONOGENEAN HARDPART using machine learning techniques.

BMC bioinformatics
BACKGROUND: Studying structural and functional morphology of small organisms such as monogenean, is difficult due to the lack of visualization in three dimensions. One possible way to resolve this visualization issue is to create digital 3D models wh...

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...

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...

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...

Conflict management in the fusion of complementary segmentations of deformed kidneys and nephroblastoma.

Medical image analysis
The fusion of multiple segmentations aims to improve their accuracy in order to make them exploitable. However, conflicts may appear. In this paper, two conflict-management models are proposed for the fusion of complementary segmentations. This confl...

Development of a robot-assisted ultrasound-guided radiation therapy (USgRT).

International journal of computer assisted radiology and surgery
PURPOSE: Radiation treatment is improved by the use of image-guided workflows. This work pursues the approach of using ultrasound (US) as a real-time imaging modality. The primary focus of this study is to develop and test a breathing and motion cont...

A novel machine learning based computational framework for homogenization of heterogeneous soft materials: application to liver tissue.

Biomechanics and modeling in mechanobiology
Real-time simulation of organs increases comfort and safety for patients during the surgery. Proper generalized decomposition (PGD) is an efficient numerical method with coordinate errors below 1 mm and response time below 0.1 s that can be used for ...

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images.

NeuroImage. Clinical
Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. It would also greatly facilitate the study of brain-behavior relationships by...