Abstract: In this paper, we propose a real-time image superpixel segmentation method with 50 frames/s by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. In ...

Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...

A new artificial intelligence (AI) tool could make it much easier-and cheaper-for doctors and researchers to train medical imaging software, even when only a small number of patient scans are ...

Code repository for training a brain tumour U-Net 3D image segmentation model using the 'Task1 Brain Tumour' medical segmentation decathlon challenge dataset.

VAN-GAN: Vessel Segmentation Generative Adversarial Network. A tool to segment 3D vascular networks without paired training data. Code repository for training a brain tumour U-Net 3D image ...

Medical image segmentation is at the heart of modern healthcare AI, enabling crucial tasks such as disease detection, progression monitoring, and personalized treatment planning. In disciplines like ...

Introduction: Laryngeal high-speed video (HSV) is a widely used technique for diagnosing laryngeal diseases. Among various analytical approaches, segmentation of glottis regions has proven effective ...

Abstract: Traditional substation inspection system has the problems of low inspection accuracy and low inspection efficiency. In this paper, an image recognition algorithm based on improved DBSCAN ...

The color image of the fire hole is key for the working condition identification of the aluminum electrolysis cell (AEC). However, the image of the fire hole is difficult for image segmentation due to ...