Ore Image Segmentation Method Based on U-Net and Watershed

Ore image segmentation is a key step in an ore grain size analysis based on image processing.The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and over-segmentation.In this article,in order to solve the problem,an ore image segmentation method based on U-Net is proposed.We adjust the structure of U-Net to speed up the processing,and we modify the loss function to enhance the generalization of the model.After the collection of the ore image,we design the annotation standard and train the network with the annotated image.Finally,the marked watershed algorithm is used to segment the adhesion area.The experimental results show that the proposed method has the characteristics of fast speed,strong robustness and high precision.It has great practical value to the actual ore grain statistical task.

Advances in Deep Learning-Based Medical Image Analysis

Importance.With the booming growth of artificial intelligence(AI),especially the recent advancements of deep learning,utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia.This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications.It also discussed the existing problems in the field and provided possible solutions and future directions.Highlights.This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications.More specifically,state-ofthe-art clinical applications include four major human body systems:the nervous system,the cardiovascular system,the digestive system,and the skeletal system.Overall,according to the best available evidence,deep learning models performed well in medical image analysis,but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability.Future direction could include federated learning,benchmark dataset collection,and utilizing domain subject knowledge as priors.Conclusion.Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy,efficiency,stability,and scalability.Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.