Deformable Meshes for Medical Image Segmentation: Accurate Automatic Segmentation of Anatomical Structures (Aktuelle Forschung Medizintechnik - Latest Research in Medical Engineering)
暫譯: 醫學影像分割的可變形網格:解剖結構的準確自動分割(當前醫療技術研究 - 醫療工程最新研究)

Dagmar Kainmueller

  • 出版商: Springer
  • 出版日期: 2014-08-29
  • 售價: $2,430
  • 貴賓價: 9.5$2,309
  • 語言: 英文
  • 頁數: 200
  • 裝訂: Paperback
  • ISBN: 3658070145
  • ISBN-13: 9783658070144
  • 海外代購書籍(需單獨結帳)

商品描述

​ Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author’s core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatomical structures, together with thorough evaluations of segmentation accuracy on clinical image data. As compared to related work, these fully automatic pipelines allow for highly accurate segmentation of benchmark image data.​

商品描述(中文翻譯)

在醫學影像數據中,解剖結構的分割是一項在臨床實踐中至關重要的任務。Dagmar Kainmueller 介紹了在 3D 醫學影像數據中進行準確全自動解剖結構分割的方法。作者的核心方法貢獻是一種新穎的變形模型,克服了當前最先進的可變形表面方法的限制,因此能夠準確分割解剖結構的尖端和脊狀特徵。至於實際貢獻,她提出了針對多種解剖結構的應用特定分割流程,並對臨床影像數據的分割準確性進行了徹底評估。與相關工作相比,這些全自動流程能夠對基準影像數據進行高度準確的分割。

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