AIMC Topic: Image Processing, Computer-Assisted

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A Parallel Convolutional Network Based on Spiking Neural Systems.

International journal of neural systems
Deep convolutional neural networks have shown advanced performance in accurately segmenting images. In this paper, an SNP-like convolutional neuron structure is introduced, abstracted from the nonlinear mechanism in nonlinear spiking neural P (NSNP) ...

US2Mask: Image-to-mask generation learning via a conditional GAN for cardiac ultrasound image segmentation.

Computers in biology and medicine
Cardiac ultrasound (US) image segmentation is vital for evaluating clinical indices, but it often demands a large dataset and expert annotations, resulting in high costs for deep learning algorithms. To address this, our study presents a framework ut...

Damage detection of road domain waveform guardrail structure based on machine learning multi-module fusion.

PloS one
The current highway waveform guardrail recognition technology has encountered problems with low segmentation accuracy and strong noise interference. Therefore, an improved U-net semantic segmentation model is proposed to improve the efficiency of roa...

A Data Augmentation Methodology to Reduce the Class Imbalance in Histopathology Images.

Journal of imaging informatics in medicine
Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and mul...

Artifact reduction in photoacoustic images by generating virtual dense array sensor from hemispheric sparse array sensor using deep learning.

Journal of medical ultrasonics (2001)
PURPOSE: Vascular distribution is important information for diagnosing diseases and supporting surgery. Photoacoustic imaging is a technology that can image blood vessels noninvasively and with high resolution. In photoacoustic imaging, a hemispheric...

NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.

Scientific data
In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior s...

Suppressing HIFU interference in ultrasound images using 1D U-Net-based neural networks.

Physics in medicine and biology
One big challenge with high-intensity focused ultrasound (HIFU) is that the intense acoustic interference generated by HIFU irradiation overwhelms the B-mode monitoring images, compromising monitoring effectiveness. This study aims to overcome this p...

Virtual staining for histology by deep learning.

Trends in biotechnology
In pathology and biomedical research, histology is the cornerstone method for tissue analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time for staining procedures. Deep learning is now enabling digital replaceme...

Machine learning aided single cell image analysis improves understanding of morphometric heterogeneity of human mesenchymal stem cells.

Methods (San Diego, Calif.)
The multipotent stem cells of our body have been largely harnessed in biotherapeutics. However, as they are derived from multiple anatomical sources, from different tissues, human mesenchymal stem cells (hMSCs) are a heterogeneous population showing ...

Dual-channel neural network for instance segmentation of synapse.

Computers in biology and medicine
Detection and segmentation of neural synapses in electron microscopy images are the committed steps for analyzing neural ultrastructure. To date, manual annotation of the structure in synapses has been the primary method, which is time-consuming and ...