Abstract: In recent years, deep learning-based image segmentation technology has obtained outstanding achievements in typical jobs, and an increasing number of application fields require efficient and ...

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 ...

tumor cases and BI-RADS annotations in categories 2, 3, 4, and 5. In addition, the dataset also contains ground truth delineations that divide the BUS images into ...

Threshold-based segmentation by selecting a target color vector in one of six color spaces (RGB, HSV, CIELAB, CIEXYZ, YCbCr or YIQ (NTSC)) and isolating pixels within a user-specified tolerance.

Introduction: Pore space in tight sandstone formation is very complex with micro-scale and nano-scale pores/throats, the multi-scale characteristics needs to be considered for the construction of ...

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 ...

Abstract: Image segmentation plays an important role in image processing. Image segmentation algorithms have been proposed as early as the last century, and constantly find and optimize various ...