自动前列腺分割评估：专家分割最佳数量这一复杂问题。 - Suppr | 超能文献

Evaluation of automated prostate segmentation: The complex issue of the optimal number of expert segmentations.

作者信息

Rouvière Olivier

机构信息

Hospices Civils de Lyon, Department of Radiology, Hôpital Edouard Herriot, Lyon 69437, France; Université de Lyon, Lyon, France; Université Lyon 1, Lyon, France; Faculté de Médecine Lyon Est, Lyon 69003, France; LabTau, INSERM U1032, Lyon 69003, France.

出版信息

Diagn Interv Imaging. 2024 Feb;105(2):45-46. doi: 10.1016/j.diii.2023.10.002. Epub 2023 Oct 19.

DOI:10.1016/j.diii.2023.10.002

Abstract

摘要

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

相似文献

1

Evaluation of automated prostate segmentation: The complex issue of the optimal number of expert segmentations.自动前列腺分割评估：专家分割最佳数量这一复杂问题。

Diagn Interv Imaging. 2024 Feb;105(2):45-46. doi: 10.1016/j.diii.2023.10.002. Epub 2023 Oct 19.

2

Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation.基于模型和深度学习的T2加权磁共振图像前列腺自动三维分区联合分割：临床评估

Eur Radiol. 2022 May;32(5):3248-3259. doi: 10.1007/s00330-021-08408-5. Epub 2022 Jan 10.

3

Spatially varying accuracy and reproducibility of prostate segmentation in magnetic resonance images using manual and semiautomated methods.使用手动和半自动方法在磁共振图像中前列腺分割的空间变化准确性和可重复性。

Med Phys. 2014 Nov;41(11):113503. doi: 10.1118/1.4899182.

4

Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI.评价自动分割算法的参考标准：MRI 上手动勾画前列腺轮廓的观察者间变异性的量化。

Diagn Interv Imaging. 2024 Feb;105(2):65-73. doi: 10.1016/j.diii.2023.08.001. Epub 2023 Aug 21.

5

Manual prostate MRI segmentation by readers with different experience: a study of the learning progress.手动前列腺 MRI 分割：不同经验读者的研究——学习进展。

Eur Radiol. 2024 Jul;34(7):4801-4809. doi: 10.1007/s00330-023-10515-4. Epub 2024 Jan 2.

6

Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging.磁共振成像中前列腺自动分割方法的准确性验证。

J Digit Imaging. 2017 Dec;30(6):782-795. doi: 10.1007/s10278-017-9964-7.

7

Estimation of inferential uncertainty in assessing expert segmentation performance from STAPLE.从 STAPLE 评估专家分割性能的推断不确定性估计。

IEEE Trans Med Imaging. 2010 Mar;29(3):771-80. doi: 10.1109/TMI.2009.2036011.

8

Speckle-initialized dynamic segmentation of the prostate.基于散斑初始化的前列腺动态分割

Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6352-5. doi: 10.1109/IEMBS.2009.5333266.

9

Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks.基于全卷积神经网络的磁共振前列腺多区域自动分割。

Eur Radiol. 2023 Jul;33(7):5087-5096. doi: 10.1007/s00330-023-09410-9. Epub 2023 Jan 24.

10

Comparison of automated segmentation techniques for magnetic resonance images of the prostate.前列腺磁共振图像自动分割技术比较。

BMC Med Imaging. 2023 Feb 11;23(1):32. doi: 10.1186/s12880-023-00974-y.

引用本文的文献

1

Defining ground truth for prostate segmentation of transrectal ultrasound images: Inter- and intra-observer variability of manual versus semi-automatic methods.定义经直肠超声图像前列腺分割的真实标准：手动与半自动方法的观察者间和观察者内变异性

Med Phys. 2025 Aug;52(8):e18025. doi: 10.1002/mp.18025.

2

Deep learning-based segmentation of kidneys and renal cysts on T2-weighted MRI from patients with autosomal dominant polycystic kidney disease.基于深度学习的常染色体显性多囊肾病患者 T2 加权 MRI 上肾脏和肾囊肿的分割。

Eur Radiol Exp. 2024 Oct 30;8(1):122. doi: 10.1186/s41747-024-00520-7.