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A new image-based method improves accuracy in measuring hair loss in mice.

This study describes a new method using ImageJ to quantitatively assess hair loss in mice with alopecia areata, offering a more precise and reproducible alternative to traditional visual scoring systems by employing image-based analysis techniques.

This study presents a new hair removal algorithm for dermatoscopic images of skin lesions that improves hair detection accuracy by 2–7% and hair repair accuracy by 2–5% on average, using advanced techniques like maximum variance fuzzy clustering, Criminisi priorities, and the ant colony algorithm.

This study found that metabolite levels in hair samples differ significantly by hair color and segment, leading to recommendations for best practices in hair metabolomic studies.

In this study, the proposed MSF-VMDNet, combining dual encoder networks with a multi-frequency domain mechanism, significantly outperformed existing methods in segmenting skin cancer tissues from histological slide images, achieving high accuracy with an MIoU of 95.37% and a Dice coefficient of 95.11%.