{"id":"doi:10.1007/s10278-019-00227-x","kind":"review","title":"Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges","authors":["Mohammad Hesam Hesamian","Wenjing Jia","Xiangjian He","Paul J Kennedy"],"author_count":4,"year":2019,"published":"2019-05-29","venue":"Journal of Imaging Informatics in Medicine","cited_by":1638,"doi":"10.1007/s10278-019-00227-x","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1007/s10278-019-00227-x","archive_url":"https://space-kitty.com/papers/doi:10.1007/s10278-019-00227-x","updated":null,"pmid":"31144149","pmcid":null,"openalex_id":"W2947263797","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10278-019-00227-x.pdf","abstract":"Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Medical Image Segmentation Techniques"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges\" by Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He et al., https://doi.org/10.1007/s10278-019-00227-x, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"ai-medicine","name":"AI in Clinical Medicine","domain_slug":"medicine"}],"cited_in_dossiers":[]}