{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fully-convolutional-architectures-for-multi","title":"Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs","arxiv_id":"1701.08816","date":"2017-01-30","proceeding":null,"authors":["Alexey A. Novikov","Dimitrios Lenis","David Major","Jiri Hladůvka","Maria Wimmer","Katja Bühler"],"abstract":"The success of deep convolutional neural networks on image classification and\nrecognition tasks has led to new applications in very diversified contexts,\nincluding the field of medical imaging. In this paper we investigate and\npropose neural network architectures for automated multi-class segmentation of\nanatomical organs in chest radiographs, namely for lungs, clavicles and heart.\nWe address several open challenges including model overfitting, reducing number\nof parameters and handling of severely imbalanced data in CXR by fusing recent\nconcepts in convolutional networks and adapting them to the segmentation\nproblem task in CXR. We demonstrate that our architecture combining delayed\nsubsampling, exponential linear units, highly restrictive regularization and a\nlarge number of high resolution low level abstract features outperforms\nstate-of-the-art methods on all considered organs, as well as the human\nobserver on lungs and heart. The models use a multi-class configuration with\nthree target classes and are trained and tested on the publicly available JSRT\ndatabase, consisting of 247 X-ray images the ground-truth masks for which are\navailable in the SCR database. Our best performing model, trained with the loss\nfunction based on the Dice coefficient, reached mean Jaccard overlap scores of\n95.0\\% for lungs, 86.8\\% for clavicles and 88.2\\% for heart. This architecture\noutperformed the human observer results for lungs and heart.","url_abs":"http://arxiv.org/abs/1701.08816v4","url_pdf":"http://arxiv.org/pdf/1701.08816v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fully-convolutional-architectures-for-multi","repo_url":"https://github.com/Diganta13/Image-segmentation-by-UNet-Algorithm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}