Deep learning approaches to biomedical image segmentation

By I. R. I. Haque, J. Neubert. 2020

412 citations recorded by the source corpus.

Abstract The review covers automatic segmentation of images by means of deep learning approaches in the area of medical imaging. Current developments in machine learning, particularly related to deep learning, are proving instrumental in identification, and quantification of patterns in the medical images. The pivotal point of these advancements is the essential capability of the deep learning approaches to obtain hierarchical feature representations directly from the images, which in turn is eliminating the need for handcrafted features. Deep learning is expeditiously turning into the state-of-the-art for medical image processing and has resulted in performance improvements in diverse clinical applications. In this review, the basics of deep learning methods are discussed along with an overview of successful implementations involving image segmentation for different medical applications. Finally, some research issues are highlighted and the future need for further improvements is pointed out.

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/e95513a8a6fbd81b086bad3fdc5ea38c6e615020

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In Computer Vision and Pattern Recognition

In Neurology

Citations in the corpus

Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey (2020, background): “From the autonomous car driving [36] to medical diagnosis [37, 38], the requirement of the task of image segmentation is everywhere.”
Diffusion Models for Implicit Image Segmentation Ensembles (2021, background): “Semantic segmentation is an important and well-explored area in medical image analysis (Rizwan I Haque and Neubert, 2020).”