AIMC Topic: Image Processing, Computer-Assisted

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Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies.

Communications biology
Multiplexed imaging techniques require identifying different cell types in the tissue. To utilize their potential for cellular and molecular analysis, high throughput and accurate analytical approaches are needed in parsing vast amounts of data, part...

Pixel super-resolved virtual staining of label-free tissue using diffusion models.

Nature communications
Virtual staining of tissue offers a powerful tool for transforming label-free microscopy images of unstained tissue into equivalents of histochemically stained samples. This study presents a diffusion model-based pixel super-resolution virtual staini...

Gaussian random fields as an abstract representation of patient metadata for multimodal medical image segmentation.

Scientific reports
Growing rates of chronic wound occurrence, especially in patients with diabetes, has become a recent concerning trend. Chronic wounds are difficult and costly to treat, and have become a serious burden on health care systems worldwide. Innovative dee...

Classification of biomedical lung cancer images using optimized binary bat technique by constructing oblique decision trees.

Scientific reports
Due to imbalanced data values and high-dimensional features of lung cancer from CT scans images creates significant challenges in clinical research. The improper classification of these images leads towards higher complexity in classification process...

An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution.

Scientific reports
High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinical work. Deep learning-based single-image super-resolution (SISR) techn...

RETINA: Reconstruction-based pre-trained enhanced TransUNet for electron microscopy segmentation on the CEM500K dataset.

PLoS computational biology
Electron microscopy (EM) has revolutionized our understanding of cellular structures at the nanoscale. Accurate image segmentation is required for analyzing EM images. While manual segmentation is reliable, it is labor-intensive, incentivizing the de...

Automatic identification of Parkinsonism using clinical multi-contrast brain MRI: a large self-supervised vision foundation model strategy.

EBioMedicine
BACKGROUND: Valid non-invasive biomarkers for Parkinson's disease (PD) and Parkinson-plus syndrome (PPS) are urgently needed. Based on our recent self-supervised vision foundation model the Shift Window UNET TRansformer (Swin UNETR), which uses clini...

Radiomics applications in the modern management of esophageal squamous cell carcinoma.

Medical oncology (Northwood, London, England)
Esophageal cancer ranks among the most lethal malignancies globally, with China accounting for more than half of worldwide esophageal squamous cell carcinoma (ESCC) cases. Late-stage diagnosis frequently precludes surgical intervention, contributing ...

Generating dermatopathology reports from gigapixel whole slide images with HistoGPT.

Nature communications
Histopathology is the reference standard for diagnosing the presence and nature of many diseases, including cancer. However, analyzing tissue samples under a microscope and summarizing the findings in a comprehensive pathology report is time-consumin...

A new dataset for measuring the performance of blood vessel segmentation methods under distribution shifts.

PloS one
Creating a dataset for training supervised machine learning algorithms can be a demanding task. This is especially true for blood vessel segmentation since one or more specialists are usually required for image annotation, and creating ground truth l...