AIMC Topic: Image Processing, Computer-Assisted

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Deep learning-based reconstruction of interventional tools and devices from four X-ray projections for tomographic interventional guidance.

Medical physics
PURPOSE: Image guidance for minimally invasive interventions is usually performed by acquiring fluoroscopic images using a monoplanar or a biplanar C-arm system. However, the projective data provide only limited information about the spatial structur...

Weakly supervised underwater fish segmentation using affinity LCFCN.

Scientific reports
Estimating fish body measurements like length, width, and mass has received considerable research due to its potential in boosting productivity in marine and aquaculture applications. Some methods are based on manual collection of these measurements ...

AAWS-Net: Anatomy-aware weakly-supervised learning network for breast mass segmentation.

PloS one
Accurate segmentation of breast masses is an essential step in computer aided diagnosis of breast cancer. The scarcity of annotated training data greatly hinders the model's generalization ability, especially for the deep learning based methods. Howe...

Deep learning based spectral CT imaging.

Neural networks : the official journal of the International Neural Network Society
Spectral computed tomography (CT) has attracted much attention in radiation dose reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray energy spectrum is divided into several bins, each energy-bin-specific p...

SCU-Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms.

Medical physics
PURPOSE: Measurements of breast arterial calcifications (BAC) can offer a personalized, non-invasive approach to risk-stratify women for cardiovascular diseases such as heart attack and stroke. We aim to detect and segment breast arterial calcificati...

MedmeshCNN - Enabling meshcnn for medical surface models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: MeshCNN is a recently proposed Deep Learning framework that drew attention due to its direct operation on irregular, non-uniform 3D meshes. It outperformed state-of-the-art methods in classification and segmentation tasks of...

Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.

Physics in medicine and biology
Efficient, reliable and reproducible target volume delineation is a key step in the effective planning of breast radiotherapy. However, post-operative breast target delineation is challenging as the contrast between the tumor bed volume (TBV) and nor...

X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing.

Biomedical physics & engineering express
This work proposes a pixel-classification approach for vessel segmentation in x-ray angiograms. The proposal uses textural features such as anisotropic diffusion, features based on the Hessian matrix, mathematical morphology and statistics. These fea...

Validation of a Whole Heart Segmentation from Computed Tomography Imaging Using a Deep-Learning Approach.

Journal of cardiovascular translational research
The aim of this study is to develop an automated deep-learning-based whole heart segmentation of ECG-gated computed tomography data. After 21 exclusions, CT acquired before transcatheter aortic valve implantation in 71 patients were reviewed and rand...

Deep learning-based parameter estimation in fetal diffusion-weighted MRI.

NeuroImage
Diffusion-weighted magnetic resonance imaging (DW-MRI) of fetal brain is challenged by frequent fetal motion and signal to noise ratio that is much lower than non-fetal imaging. As a result, accurate and robust parameter estimation in fetal DW-MRI re...