{"title":"用于内耳组织分割的超分辨率分割网络。","authors":"Ziteng Liu, Yubo Fan, Ange Lou, Jack H Noble","doi":"10.1007/978-3-031-44689-4_2","DOIUrl":null,"url":null,"abstract":"<p><p>Cochlear implants (CIs) are considered the standard-of-care treatment for profound sensory-based hearing loss. Several groups have proposed computational models of the cochlea in order to study the neural activation patterns in response to CI stimulation. However, most of the current implementations either rely on high-resolution histological images that cannot be customized for CI users or CT images that lack the spatial resolution to show cochlear structures. In this work, we propose to use a deep learning-based method to obtain μCT level tissue labels using patient CT images. Experiments showed that the proposed super-resolution segmentation architecture achieved very good performance on the inner-ear tissue segmentation. Our best-performing model (0.871) outperformed the UNet (0.746), VNet (0.853), nnUNet (0.861), TransUNet (0.848), and SRGAN (0.780) in terms of mean dice score.</p>","PeriodicalId":91967,"journal":{"name":"Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10979466/pdf/","citationCount":"0","resultStr":"{\"title\":\"Super-resolution segmentation network for inner-ear tissue segmentation.\",\"authors\":\"Ziteng Liu, Yubo Fan, Ange Lou, Jack H Noble\",\"doi\":\"10.1007/978-3-031-44689-4_2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Cochlear implants (CIs) are considered the standard-of-care treatment for profound sensory-based hearing loss. Several groups have proposed computational models of the cochlea in order to study the neural activation patterns in response to CI stimulation. However, most of the current implementations either rely on high-resolution histological images that cannot be customized for CI users or CT images that lack the spatial resolution to show cochlear structures. In this work, we propose to use a deep learning-based method to obtain μCT level tissue labels using patient CT images. Experiments showed that the proposed super-resolution segmentation architecture achieved very good performance on the inner-ear tissue segmentation. Our best-performing model (0.871) outperformed the UNet (0.746), VNet (0.853), nnUNet (0.861), TransUNet (0.848), and SRGAN (0.780) in terms of mean dice score.</p>\",\"PeriodicalId\":91967,\"journal\":{\"name\":\"Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10979466/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/978-3-031-44689-4_2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/10/7 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-031-44689-4_2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/10/7 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
人工耳蜗(CI)被认为是治疗深度感官性听力损失的标准方法。一些研究小组提出了耳蜗计算模型,以研究神经激活模式对 CI 刺激的反应。然而,目前大多数的实现要么依赖于无法为 CI 用户定制的高分辨率组织学图像,要么依赖于缺乏空间分辨率以显示耳蜗结构的 CT 图像。在这项工作中,我们建议使用基于深度学习的方法,利用患者的 CT 图像获取 μCT 级别的组织标签。实验表明,所提出的超分辨率分割架构在内耳组织分割方面取得了非常好的性能。就平均骰子得分而言,我们表现最好的模型(0.871)优于 UNet(0.746)、VNet(0.853)、nnUNet(0.861)、TransUNet(0.848)和 SRGAN(0.780)。
Super-resolution segmentation network for inner-ear tissue segmentation.
Cochlear implants (CIs) are considered the standard-of-care treatment for profound sensory-based hearing loss. Several groups have proposed computational models of the cochlea in order to study the neural activation patterns in response to CI stimulation. However, most of the current implementations either rely on high-resolution histological images that cannot be customized for CI users or CT images that lack the spatial resolution to show cochlear structures. In this work, we propose to use a deep learning-based method to obtain μCT level tissue labels using patient CT images. Experiments showed that the proposed super-resolution segmentation architecture achieved very good performance on the inner-ear tissue segmentation. Our best-performing model (0.871) outperformed the UNet (0.746), VNet (0.853), nnUNet (0.861), TransUNet (0.848), and SRGAN (0.780) in terms of mean dice score.