{"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/splenomegaly-segmentation-using-global","title":"Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks","arxiv_id":"1712.00542","date":"2017-12-02","proceeding":null,"authors":["Yuankai Huo","Zhoubing Xu","Shunxing Bao","Camilo Bermudez","Andrew J. Plassard","Jiaqi Liu","Yuang Yao","Albert Assad","Richard G. Abramson","Bennett A. Landman"],"abstract":"Spleen volume estimation using automated image segmentation technique may be\nused to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance\nImaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN)\nsegmentation methods have demonstrated advantages for abdominal organ\nsegmentation. However, variations in both size and shape of the spleen on MRI\nimages may result in large false positive and false negative labeling when\ndeploying DCNN based methods. In this paper, we propose the Splenomegaly\nSegmentation Network (SSNet) to address spatial variations when segmenting\nextraordinarily large spleens. SSNet was designed based on the framework of\nimage-to-image conditional generative adversarial networks (cGAN).\nSpecifically, the Global Convolutional Network (GCN) was used as the generator\nto reduce false negatives, while the Markovian discriminator (PatchGAN) was\nused to alleviate false positives. A cohort of clinically acquired 3D MRI scans\n(both T1 weighted and T2 weighted) from patients with splenomegaly were used to\ntrain and test the networks. The experimental results demonstrated that a mean\nDice coefficient of 0.9260 and a median Dice coefficient of 0.9262 using SSNet\non independently tested MRI volumes of patients with splenomegaly.","url_abs":"http://arxiv.org/abs/1712.00542v1","url_pdf":"http://arxiv.org/pdf/1712.00542v1.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":"splenomegaly-segmentation-using-global","repo_url":"https://github.com/MASILab/SSNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"organ-segmentation","task_name":"Organ Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dcnn","method_name":"DCNN"},{"method_slug":"global-convolutional-network","method_name":"Global Convolutional Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}