{"title":"上下文相加网络,有效提升3D图像分割。","authors":"Zhenlin Xu, Zhengyang Shen, Marc Niethammer","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Semantic segmentation for 3D medical images is an important task for medical image analysis which would benefit from more efficient approaches. We propose a 3D segmentation framework of cascaded fully convolutional networks (FCNs) with contextual inputs and additive outputs. Compared to previous contextual cascaded networks the additive output forces each subsequent model to refine the output of previous models in the cascade. We use U-Nets of various complexity as elementary FCNs and demonstrate our method for cartilage segmentation on a large set of 3D magnetic resonance images (MRI) of the knee. We show that a cascade of simple U-Nets may for certain tasks be superior to a single deep and complex U-Net with almost two orders of magnitude more parameters. Our framework also allows greater flexibility in trading-off performance and efficiency during testing and training.</p>","PeriodicalId":92501,"journal":{"name":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S...","volume":"11045 ","pages":"92-100"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6590074/pdf/nihms-1033318.pdf","citationCount":"0","resultStr":"{\"title\":\"Contextual Additive Networks to Efficiently Boost 3D Image Segmentations.\",\"authors\":\"Zhenlin Xu, Zhengyang Shen, Marc Niethammer\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Semantic segmentation for 3D medical images is an important task for medical image analysis which would benefit from more efficient approaches. We propose a 3D segmentation framework of cascaded fully convolutional networks (FCNs) with contextual inputs and additive outputs. Compared to previous contextual cascaded networks the additive output forces each subsequent model to refine the output of previous models in the cascade. We use U-Nets of various complexity as elementary FCNs and demonstrate our method for cartilage segmentation on a large set of 3D magnetic resonance images (MRI) of the knee. We show that a cascade of simple U-Nets may for certain tasks be superior to a single deep and complex U-Net with almost two orders of magnitude more parameters. Our framework also allows greater flexibility in trading-off performance and efficiency during testing and training.</p>\",\"PeriodicalId\":92501,\"journal\":{\"name\":\"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S...\",\"volume\":\"11045 \",\"pages\":\"92-100\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6590074/pdf/nihms-1033318.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S...\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2018/9/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S...","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/9/20 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Contextual Additive Networks to Efficiently Boost 3D Image Segmentations.
Semantic segmentation for 3D medical images is an important task for medical image analysis which would benefit from more efficient approaches. We propose a 3D segmentation framework of cascaded fully convolutional networks (FCNs) with contextual inputs and additive outputs. Compared to previous contextual cascaded networks the additive output forces each subsequent model to refine the output of previous models in the cascade. We use U-Nets of various complexity as elementary FCNs and demonstrate our method for cartilage segmentation on a large set of 3D magnetic resonance images (MRI) of the knee. We show that a cascade of simple U-Nets may for certain tasks be superior to a single deep and complex U-Net with almost two orders of magnitude more parameters. Our framework also allows greater flexibility in trading-off performance and efficiency during testing and training.