AIMC Topic: Image Processing, Computer-Assisted

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A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning.

Physics in medicine and biology
. Range uncertainty is a major concern affecting the delivery precision in proton therapy. The Compton camera (CC)-based prompt-gamma (PG) imaging is a promising technique to provide 3Drange verification. However, the conventional back-projected PG i...

Hepatic vessels segmentation using deep learning and preprocessing enhancement.

Journal of applied clinical medical physics
PURPOSE: Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of su...

Framework for dual-energy-like chest radiography image synthesis from single-energy computed tomography based on cycle-consistent generative adversarial network.

Medical physics
BACKGROUND: Dual-energy (DE) chest radiography (CXR) enables the selective imaging of two relevant materials, namely, soft tissue and bone structures, to better characterize various chest pathologies (i.e., lung nodule, bony lesions, etc.) and potent...

A deep learning-based framework for retinal fundus image enhancement.

PloS one
PROBLEM: Low-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis.

HGM-cNet: Integrating hippocampal gray matter probability map into a cascaded deep learning framework improves hippocampus segmentation.

European journal of radiology
A robust cascaded deep learning framework with integrated hippocampal gray matter (HGM) probability map was developed to improve the hippocampus segmentation (called HGM-cNet) due to its significance in various neuropsychiatric disorders such as Alzh...

CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising.

Physics in medicine and biology
. Low-dose computed tomography (LDCT) denoising is an important problem in CT research. Compared to the normal dose CT, LDCT images are subjected to severe noise and artifacts. Recently in many studies, vision transformers have shown superior feature...

Moving perfusion culture and live-cell imaging from lab to disc: proof of concept toxicity assay with AI-based image analysis.

Lab on a chip
, cell-based assays are essential in diagnostics and drug development. There are ongoing efforts to establish new technologies that enable real-time detection of cell-drug interaction during culture under flow conditions. Our compact (10 × 10 × 8.5 c...

Validation of a Fully Automated Hybrid Deep Learning Cardiac Substructure Segmentation Tool for Contouring and Dose Evaluation in Lung Cancer Radiotherapy.

Clinical oncology (Royal College of Radiologists (Great Britain))
BACKGROUND AND PURPOSE: Accurate and consistent delineation of cardiac substructures is challenging. The aim of this work was to validate a novel segmentation tool for automatic delineation of cardiac structures and subsequent dose evaluation, with p...

Weakly supervised histopathology image segmentation with self-attention.

Medical image analysis
Accurate segmentation in histopathology images at pixel-level plays a critical role in the digital pathology workflow. The development of weakly supervised methods for histopathology image segmentation liberates pathologists from time-consuming and l...

Inter-individual deep image reconstruction via hierarchical neural code conversion.

NeuroImage
The sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. Although anatomical and fu...