AIMC Topic: Image Processing, Computer-Assisted

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Exploring HSI imagery for overlapped chromosomes segmentation.

Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology
The segmentation of overlapping chromosomes in metaphase images is a longstanding challenge in cytogenetics, where limited spectral contrast in conventional RGB imaging. In this study, we explore hyperspectral imaging (HSI) as a promising alternative...

R2GDN: RepGhost based residual dense network for image super-resolution.

PloS one
This study introduces a novel lightweight image super-resolution reconstruction network aimed at mitigating the challenges associated with computational complexity and memory consumption in existing super-resolution reconstruction networks. The propo...

Unsupervised discovery of ischemic stroke phenotypes from multimodal MRI radiomics.

Biomedical physics & engineering express
This study presents a fully unsupervised and label-independent radiomic pipeline designed to group different types of ischemic stroke lesions using multimodal Magnetic Resonance Imaging (MRI) . The aim is to address lesion heterogeneity and the absen...

MLGF-GAN: a multi-level local-global feature fusion GAN for OCT image super-resolution.

Biomedical physics & engineering express
Optical coherence tomography (OCT), a non-invasive imaging modality, holds significant clinical value in cardiology and ophthalmology. However, its imaging quality is often constrained by inherently limited resolution, thereby affecting diagnostic ut...

Incorporating and quantifying deformable image registration uncertainties in dose accumulation: a feasibility study on the benefit of online adaptive therapy.

Physics in medicine and biology
. Accurate dose accumulation relies on deformable image registration (DIR) to track dose across multiple images. However, DIR introduces uncertainties that can impact cumulative dose distributions. In this study, we present a probabilistic framework ...

Population heterogeneity revealed in morphometric analysis of densely populated microbial swarm collectives.

mBio
UNLABELLED: Uncovering cell morphology within communities is crucial to understanding how collective groups of organisms can function and adapt to their environments. Key questions remain regarding how cell morphology influences population behaviors ...

DDU-Net: learning complex vascular topologies with KAN-Swin transformers and double dynamic upsampler.

Biomedical physics & engineering express
To segment complex vascular topologies in Optical Coherence Tomography Angiography (OCTA), we introduce DDU-Net. This work addresses the theoretical limitations of standard Swin Transformers, whose internal Multi-Layer Perceptron (MLP) blocks use fix...

Model-based spatiotemporal synthetic data generation framework and deep-learning reconstruction for real-time MRI oxygen extraction fraction mapping.

Physics in medicine and biology
Synthetic data has emerged as a highly efficient solution to address the scarcity of training data in deep learning-based quantitative magnetic resonance imaging (qMRI) reconstruction. However, current applications of synthetic data predominantly foc...

A self-supervised learning method for detection of retinitis pigmentosa and Stargardt disease.

Scientific reports
Retinitis pigmentosa (RP) and Stargardt Disease (STGD) are inherited retinal diseases that can seriously affect vision. In this study, we present a novel, two-phase self-supervised learning method that addresses the challenge of limited labeled data ...

Volumetric localization microscopy with deep learning.

Nature communications
Super-resolution microscopy, particularly localization-based methods, necessitates careful balancing of optical complexity, computational demands, and user accessibility. Conventional strategies typically adopt either deterministic or learning-based ...