AIMC Topic: Image Processing, Computer-Assisted

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A CT-free deep-learning-based attenuation and scatter correction for copper-64 PET in different time-point scans.

Radiological physics and technology
This study aimed to develop and evaluate a deep-learning model for attenuation and scatter correction in whole-body 64Cu-based PET imaging. A swinUNETR model was implemented using the MONAI framework. Whole-body PET-nonAC and PET-CTAC image pairs wer...

ICPPNet: A semantic segmentation network model based on inter-class positional prior for scoliosis reconstruction in ultrasound images.

Journal of biomedical informatics
OBJECTIVE: Considering the radiation hazard of X-ray, safer, more convenient and cost-effective ultrasound methods are gradually becoming new diagnostic approaches for scoliosis. For ultrasound images of spine regions, it is challenging to accurately...

Joint high-resolution feature learning and vessel-shape aware convolutions for efficient vessel segmentation.

Computers in biology and medicine
Clear imagery of retinal vessels is one of the critical shreds of evidence in specific disease diagnosis and evaluation, including sophisticated hierarchical topology and plentiful-and-intensive capillaries. In this work, we propose a new topology- a...

EffiViT: Hybrid CNN-Transformer for Retinal Imaging.

Computers in biology and medicine
The human eye is a vital sensory organ that is crucial for visual perception. The retina is the main component of the eye and is responsible for visual signals. Due to its characteristics, the retina can reveal the occurrence of ocular diseases. So, ...

Attention in surgical phase recognition for endoscopic pituitary surgery: Insights from real-world data.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Surgical Phase Recognition systems are used to support the automated documentation of a procedure and to provide the surgical team with real-time feedback, potentially improving surgical outcome and reducing adverse events. ...

A deep learning framework leveraging spatiotemporal feature fusion for electrophysiological source imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Electrophysiological source imaging (ESI) is a challenging technique for noninvasively measuring brain activity, which involves solving a highly ill-posed inverse problem. Traditional methods attempt to address this challen...

Atten-Nonlocal Unet: Attention and Non-local Unet for medical image segmentation.

Computers in biology and medicine
The convolutional neural network(CNN)-based models have emerged as the predominant approach for medical image segmentation due to their effective inductive bias. However, their limitation lies in the lack of long-range information. In this study, we ...

Combining Deep Data-Driven and Physics-Inspired Learning for Shear Wave Speed Estimation in Ultrasound Elastography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
The shear wave elastography (SWE) provides quantitative markers for tissue characterization by measuring the shear wave speed (SWS), which reflects tissue stiffness. SWE uses an acoustic radiation force pulse sequence to generate shear waves that pro...

Lag-Net: Lag correction for cone-beam CT via a convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Due to the presence of charge traps in amorphous silicon flat-panel detectors, lag signals are generated in consecutively captured projections. These signals lead to ghosting in projection images and severe lag artifacts in ...