AIMC Topic: Image Processing, Computer-Assisted

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Evaluation of generalization ability for deep learning-based auto-segmentation accuracy in limited field of view CBCT of male pelvic region.

Journal of applied clinical medical physics
PURPOSE: The aim of this study was to evaluate generalization ability of segmentation accuracy for limited FOV CBCT in the male pelvic region using a full-image CNN. Auto-segmentation accuracy was evaluated using various datasets with different inten...

MEDL-Net: A model-based neural network for MRI reconstruction with enhanced deep learned regularizers.

Magnetic resonance in medicine
PURPOSE: To improve the MRI reconstruction performance of model-based networks and to alleviate their large demand for GPU memory.

NVTrans-UNet: Neighborhood vision transformer based U-Net for multi-modal cardiac MR image segmentation.

Journal of applied clinical medical physics
With the rapid development of artificial intelligence and image processing technology, medical imaging technology has turned into a critical tool for clinical diagnosis and disease treatment. The extraction and segmentation of the regions of interest...

Improving image quality with deep learning image reconstruction in double-low-dose head CT angiography compared with standard dose and adaptive statistical iterative reconstruction.

The British journal of radiology
OBJECTIVE: To demonstrate similar image quality with deep learning image reconstruction (DLIR) in reduced contrast medium (CM) and radiation dose (double-low-dose) head CT angiography (CTA), in comparison with standard-dose and adaptive statistical i...

"Image quality evaluation of the Precise image CT deep learning reconstruction algorithm compared to Filtered Back-projection and iDose: a phantom study at different dose levels".

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To characterize the performance of the Precise Image (PI) deep learning reconstruction (DLR) algorithm for abdominal Computed Tomography (CT) imaging.

Improving the Neural Segmentation of Blurry Serial SEM Images by Blind Deblurring.

Computational intelligence and neuroscience
Serial scanning electron microscopy (sSEM) has recently been developed to reconstruct complex largescale neural connectomes, through learning-based instance segmentation. However, blurry images are inevitable amid prolonged automated data acquisition...

Deep learning-based accurate delineation of primary gross tumor volume of nasopharyngeal carcinoma on heterogeneous magnetic resonance imaging: A large-scale and multi-center study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: The problem of obtaining accurate primary gross tumor volume (GTVp) segmentation for nasopharyngeal carcinoma (NPC) on heterogeneous magnetic resonance imaging (MRI) images with deep learning remains unsolved. Herein, we repor...

A deep learning based dual encoder-decoder framework for anatomical structure segmentation in chest X-ray images.

Scientific reports
Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct str...

High-throughput 3DRA segmentation of brain vasculature and aneurysms using deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Automatic segmentation of the cerebral vasculature and aneurysms facilitates incidental detection of aneurysms. The assessment of aneurysm rupture risk assists with pre-operative treatment planning and enables in-silico inv...

An application of deep dual convolutional neural network for enhanced medical image denoising.

Medical & biological engineering & computing
This work investigates the medical image denoising (MID) application of the dual denoising network (DudeNet) model for chest X-ray (CXR). The DudeNet model comprises four components: a feature extraction block with a sparse mechanism, an enhancement ...