AbstractManual segmentations are considered the gold standard for ground truth in machine learning applications. Such tasks are tedious and time-consuming, albeit necessary to train reliable models. In this work, we present a dataset with expert segmentations of the prostatic zones and urethra for 200 randomly selected patients from the PROSTATEx dataset.

摘要人工分割被认为是机器学习应用中基本事实的金标准。尽管训练可靠的模型是必要的，但这样的任务既繁琐又耗时。在这项工作中，我们提供了一个数据集，其中包含从PROSTATEx数据集中随机选择的200名患者的前列腺区和尿道的专家分割。

Notably, independent duplicate segmentations were performed for 40 patients, providing inter-reader variability data. This results in a total of 240 segmentations. This dataset can be used to train machine learning models or serve as an external test set for evaluating models trained on private data, thereby addressing a current gap in the field.

。这导致总共240个分段。该数据集可用于训练机器学习模型，或用作评估在私有数据上训练的模型的外部测试集，从而解决该领域的当前差距。

The delineated structures and terminology adhere to the latest Prostate Imaging Reporting and Data Systems v2.1 guidelines, ensuring consistency..

。。

Background & SummaryProstate cancer is the most prevalent malignancy in males worldwide1, requiring accurate diagnostic and treatment approaches. For diagnosis, the global Prostate Imaging Reporting and Data Systems (PI-RADS) guidelines are used to assess prostatic lesions on multi-parametric magnetic resonance imaging (mpMRI), with different primary sequences depending on their zonal location2.

背景与总结前列腺癌是全球男性中最常见的恶性肿瘤1，需要准确的诊断和治疗方法。对于诊断，全球前列腺成像报告和数据系统（PI-RADS）指南用于评估多参数磁共振成像（mpMRI）上的前列腺病变，根据其分区位置不同的一级序列2。

These anatomical zones of the prostate are defined as the peripheral zone (PZ), central zone (CZ), transitional zone (TZ), and anterior fibromuscular stroma (AFS), and presents different characteristics and histological features3. Radiotherapy treatment of prostate cancer has traditionally been delivered with a homogeneous dose to the entire prostate.

前列腺的这些解剖区域被定义为外周区（PZ），中心区（CZ），过渡区（TZ）和前纤维肌基质（AFS），并呈现不同的特征和组织学特征3。传统上，前列腺癌的放射治疗以均匀剂量递送至整个前列腺。

Recent studies have demonstrated the efficacy of a local dose escalation to a sub volume of the prostate, a so-called focal boost4. The relationship between urethral dose and urinary toxicity highlights the importance of limiting the dose to the intraprostatic urethra to minimize side effects from treatment5,6.

最近的研究已经证明了局部剂量增加到前列腺亚体积的功效，即所谓的局灶性增强4。。

Additionally, focal boost treatment could potentially be further optimized by using the zonal information to individualize treatment and risk stratification, as the location of the cancer in different zones has different incidence, prognosis, and outcome, making treatment zonal-dependent instead of zonal-agnostic7.Manual segmentations of the prostate, its anatomical zones, and the urethra on MRI are tedious and time-consuming.

此外，由于癌症在不同区域的位置具有不同的发病率，预后和结果，因此可以通过使用区域信息来个性化治疗和风险分层来进一步优化局部增强治疗，从而使治疗区域依赖性而不是区域不可知性7.MRI上前列腺，其解剖区域和尿道的手动分割既繁琐又耗时。

Therefore, the development of an individualized, automatic method to segment the prostate, its internal zones, and the urethra is relevant in current medical practice, with implications both for treatment and diagnosis. The current literature of machine learning methods for automatic segmentations of p.

因此，开发一种个性化的，自动的方法来分割前列腺，其内部区域和尿道在当前的医疗实践中是相关的，对治疗和诊断都有影响。目前关于自动分割p的机器学习方法的文献。

Dice Similarity Coefficient (DSC):

骰子相似系数（DSC）：

The DSC measures the volumetric overlap between a reference mask and a prediction,$${DSC}=\frac{2\left|{V}_{{\rm{ref}}}\cap {V}_{{\rm{pred}}}\right|}{\left|{V}_{{\rm{ref}}}\right|+\left|{V}_{{\rm{pred}}}\right|}$$where \({V}_{{\rm{ref}}}\) and \({V}_{{\rm{pred}}}\) represent the reference and predicted volumes, respectively..

DSC测量参考掩模和预测之间的体积重叠，$${DSC}=\ frac{2 \ left|{V}_{{\rm{ref}}}\cap{V}_{{\rm{pred}}\右|}{\左|{V}_{{\rm{ref}}\右|+\左|{V}_{{\rm{pred}}\右|}$$其中\({V}_{{\rm{ref}}}和\({V}_{{\rm{pred}}}分别表示参考量和预测量。。

Hence, DSC is one for a complete overlap between the two volumes and zero for no overlap.

因此，DSC是两个卷之间完全重叠的一个，而零表示没有重叠。

Hausdorff Distance (HD):

豪斯多夫距离（HD）：

The HD is a boundary metric that calculates the maximum of all shortest distances for all voxels from one surface to the other,$${HD}({S}_{{\rm{ref}}},{S}_{{\rm{pred}}})=\max (h\left({S}_{{\rm{ref}}},{S}_{{\rm{pred}}}\right),h\left({S}_{{\rm{pred}}},{S}_{{\rm{ref}}}\right))$$where \({S}_{{\rm{ref}}}\) and \({S}_{{\rm{pred}}}\) are the reference and predicted surfaces, respectively, as derived from the original volumes, and \(h({S}_{{\rm{ref}}},\,{S}_{{\rm{pred}}})\) is given by,$$h({S}_{{\rm{ref}}},{S}_{{\rm{pred}}})=\mathop{\max }\limits_{r{\rm{\in }}{S}_{{\rm{ref}}}}\,\mathop{\min }\limits_{p{\rm{\in }}{S}_{{\rm{pred}}}}||r-p||$$where \(|\left|r-p\right||\) is the Euclidean distance..

HD是一个边界度量，它计算从一个表面到另一个表面的所有体素的所有最短距离的最大值，$${HD}({S}_{{\rm{ref}}}，{S}_{{\rm{pred}}）=\ max（h \左({S}_{{\rm{ref}}}，{S}_({S}_{{\rm{pred}}}，{S}_{{\rm{ref}}\right））$$其中\({S}_{{\rm{ref}}}和\({S}_{{\ rm{pred}}}分别是从原始体积导出的参考曲面和预测曲面，以及\（h({S}_{{\rm{ref}}}，\，{S}_{{\rm{pred}}}）\）由$$h给出({S}_{{\rm{ref}}}，{S}_{{\rm{pred}}）=\mathop{\max}\limits\uu{r{\rm{\in}{S}_{{\rm{ref}}}\，\mathop{\min}\limits\uu{p{\rm{\in}{S}_{{\rm{pred}}}| | r-p | |$$其中\（| \ left | r-p \ right | | | \）是欧几里得距离。。

Ideally, HD should be as close to zero as possible, but it is sensitive to outliers as the maximum distance of all shortest distances between the surfaces is returned.

理想情况下，HD应该尽可能接近零，但它对异常值很敏感，因为返回了曲面之间所有最短距离的最大距离。

Average Symmetric Surface Distance (ASSD):

平均对称表面距离（ASSD）：

The ASSD measures the average of all distances for every voxel from one surface to the other and vice versa. It is given by,$${ASSD}\left({S}_{{\rm{ref}}},{S}_{{\rm{pred}}}\right)=\frac{d\left({S}_{{\rm{ref}}},{S}_{{\rm{pred}}}\right)+d({S}_{{\rm{pred}}},{S}_{{\rm{ref}}})}{{N}_{{\rm{ref}}}+{N}_{{\rm{pred}}}}$$where \({S}_{{\rm{ref}}}\) and \({S}_{{\rm{pred}}}\) as well as \({N}_{{\rm{ref}}}\,\) and \({N}_{{\rm{pred}}}\) are the reference and predicted surfaces and their number of surface voxels, respectively.

ASSD测量每个体素从一个表面到另一个表面的所有距离的平均值，反之亦然。它由，$${ASSD}\ left给出({S}_{{\rm{ref}}}，{S}_{{\rm{pred}}}\右）=\ frac{d \左({S}_{{\rm{ref}}}，{S}_{{\rm{pred}}}\右）+d({S}_{{\rm{pred}}}，{S}_{{\rm{ref}}}}{{N}_{{\rm{ref}}+{N}_{{\rm{pred}}}$$其中\({S}_{{\rm{ref}}}和\({S}_{{\rm{pred}}}\）以及\({N}_{{\rm{ref}}}，\）和\({N}_{{\rm{pred}}}分别是参考曲面和预测曲面及其曲面体素的数量。

The distance \(d({S}_{{\rm{ref}}},{S}_{{\rm{pred}}})\) is determined as,$$d({S}_{{\rm{ref}}},{S}_{{\rm{pred}}})=\sum _{r\in {S}_{{\rm{ref}}}}\,\mathop{\min }\limits_{p{\rm{\in }}{S}_{{\rm{pred}}}}||r-p||$$where \(|\left|r-p\right||\) is the Euclidean distance..

距离\（d({S}_{{\rm{ref}}}，{S}_{{\rm{pred}}}）\）被确定为，$$d({S}_{{\rm{ref}}}，{S}_{{\rm{pred}}}）=\sum{r\in{S}_{{\rm{ref}}}\，\mathop{\min}\limits\uu{p{\rm{\in}{S}_{{\rm{pred}}}| | r-p | |$$其中\（| \ left | r-p \ right | | | \）是欧几里得距离。。

Like HD, ASSD would ideally be as close to zero as possible, although it is less sensitive to outliers as it represents an average over all shortest distances between the voxels of the two surfaces.

与HD一样，ASSD理想情况下尽可能接近零，尽管它对异常值不太敏感，因为它代表了两个表面体素之间所有最短距离的平均值。

Centre Line Distance (CLD):

中心线距离（CLD）：

Table 2 Inter-reader variability metrics for the 40 duplicate samples. Presented as mean ± standard deviation.Full size tableThe CLD is calculated as the ASSD for two skeletonized structures, meaning the delineations are reduced to a single voxel per slice.The CLD should ideally be as close to zero as possible as this indicates smaller deviations between the skeletonized delineations.For a more detailed analysis of the inter-reader variability, a confusion matrix is presented in Table 3.

表2 40个重复样本的读者间变异性指标。表示为平均±标准偏差。全尺寸表CLD计算为两个骨架化结构的ASSD，这意味着描绘减少到每个切片的单个体素。理想情况下，CLD应尽可能接近零，因为这表明骨架化描绘之间的偏差较小。为了更详细地分析读者之间的差异，表3列出了混淆矩阵。

The matrix shows the segmentations of Reader 1 along the rows and Reader 2 along the columns. Discrepancies are indicative of individual tendencies in the segmentation approach of each reader. Reader 1 tends to delineate a prostate that is 5% larger than that delineated by Reader 2. This enlargement is predominantly observed in the size of the PZ and CZ, which explains most of the differences observed in the matrix for these structures and the background.

该矩阵显示了读卡器1沿行和读卡器2沿列的分段。差异表明每个读者在细分方法上的个人倾向。读者1倾向于描绘比读者2描绘的前列腺大5%的前列腺。这种扩大主要在PZ和CZ的大小上观察到，这解释了在这些结构和背景的基质中观察到的大多数差异。

Reader 2 outlines a larger AFS, which then overlaps with TZ as classified by Reader 1. Other discrepancies have less individual tendencies, and most disagreements classified as adjacent structures, presumably along the borders. This is an anticipated outcome given the absence of visually distinct boundaries, underscoring the complexity of the task.Table 3 Confusion matrix derived from the inter-reader variability of the duplicate samples (n = 40), with the segmentations of Reader 1 represented along the rows and Reader 2 along the columns.Full size tableUsage NotesFor the simplest usage of the dataset, a description with Python code is available (https://github.com/UMU-DDI/ProstateZones) of how to extract the relevant images from the PROSTATEx dataset and to set up a folder structure containing images and their corresponding segmentati.

。其他差异的个人倾向较少，大多数分歧被归类为相邻结构，大概是沿着边界。鉴于没有明显的视觉界限，这是一个预期的结果，突显了任务的复杂性。表3由重复样本的读取器间变异性得出的混淆矩阵（n=40），读取器1的分段沿行表示，读取器2沿列表示。全尺寸表用法说明对于数据集的最简单用法，可以使用Python代码进行描述(https://github.com/UMU-DDI/ProstateZones)如何从PROSTATEx数据集中提取相关图像，并设置包含图像及其相应分段的文件夹结构。

Code availability

代码可用性

No code is needed to use the data, but a Python script for simple structuring is available (https://github.com/UMU-DDI/ProstateZones). The GitHub repository includes a requirements .txt file, a Python script, as well as other complementary files. Additionally, the GitHub repository includes the Hero workflow used to calculate the inter-reader variability metrics for full transparency..

使用数据不需要任何代码，但可以使用Python脚本进行简单的结构化(https://github.com/UMU-DDI/ProstateZones)。GitHub存储库包括一个requirements.txt文件、一个Python脚本以及其他补充文件。此外，GitHub存储库还包括用于计算读者间可变性度量以实现完全透明的Hero工作流。。

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Holmlund, W. et al. ProstateZones – Segmentations of the prostatic zones and urethra for the PROSTATEx dataset. Zenodo. https://doi.org/10.5281/zenodo.10718469 (2024).Reinke, A. et al. Common limitations of image processing metrics: A picture story. arXiv preprint arXiv:2104.05642 (2021).Download referencesFundingOpen access funding provided by Umea University.Author informationAuthors and AffiliationsUmeå University, Department of Diagnostics and Intervention, Umeå, SwedenWilliam Holmlund, Attila Simkó, Karin Söderkvist, Patrik Brynolfsson & Tufve NyholmUniversity of Szeged, Albert Szent-Györgyi Medical School, Department of Radiology, Szeged, HungaryPéter Palásti, Szilvia Tótin, Kamilla Kalmár, Zsófia Domoki, Zsuzsanna Fejes & Zsigmond Tamás KincsesSkåne University Hospital, Department of Haematology, Oncology and Radiation Physics, Lund, SwedenPatrik BrynolfssonAuthorsWilliam HolmlundView author publicationsYou can also search for this author in.

Holmlund，W.等人，《前列腺区域–前列腺区域和尿道的分割》，用于PROSTATEx数据集。泽诺多。https://doi.org/10.5281/zenodo.10718469（2024年）。Reinke，A.等人，《图像处理指标的常见局限性：图片故事》。。下载由Umea大学提供的参考基金开放获取资金。作者信息作者和附属机构梅奥大学，诊断与干预系，乌梅，斯维登威廉·霍姆隆德，阿提拉·西姆科，卡林·斯科德克维斯特，帕特里克·布林诺夫森和塔夫·尼霍尔姆塞格德大学，阿尔伯特·森特·戈尔吉医学院，放射科，塞格德，亨加里佩特·帕拉斯蒂，西尔维亚·托丁，卡米拉·卡尔马尔，佐菲亚·多莫基，佐佐桑纳·费耶斯和齐格蒙德·塔马斯金塞斯克瑞典隆德大学医院血液学，肿瘤学和放射物理学系Patrik BrynolfssonAuthorsWilliam HolmlundView作者出版物您也可以在中搜索这位作者。

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PubMed Google ScholarContributionsConceptualization: W.H., T.N., P.B., K.S. Data creation: P.P., S.T., K.K., Z.D., Z.F. Quality assurance: W.H., A.S., K.S., P.P., Z.F. Drafting of manuscript: W.H. All authors (W.H., A.S., K.S., P.P., S.T., K.K., Z.D., Z.F., T.Z.K., P.B., T.N.) critically reviewed and approved the final manuscript.Corresponding authorsCorrespondence to.

PubMed谷歌学术贡献概念化：W.H.，T.N.，P.B.，K.S.数据创建：P.P.，S.T.，K.K.，Z.D.，Z.F.质量保证：W.H.，A.S.，K.S.，P.P.，Z.F.稿件起草：W.H.所有作者（W.H.，A.S.，K.S.，P.P.，S.T.，K.K.，Z.D.，Z.F.，T.Z.K.，P.B.，T.N.）严格审查并批准了最终稿件。通讯作者通讯。

William Holmlund or Tufve Nyholm.Ethics declarations

威廉·霍姆隆德或塔夫·尼霍姆。道德宣言

Competing interests

相互竞争的利益

T.N. and P.B. are co-owners, while AS is employed by Hero Imaging AB, developing the software used during evaluations.

T、 N.和P.B.是共同所有者，而AS由Hero Imaging AB雇用，开发评估期间使用的软件。

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Reprints and permissionsAbout this articleCite this articleHolmlund, W., Simkó, A., Söderkvist, K. et al. ProstateZones – Segmentations of the prostatic zones and urethra for the PROSTATEx dataset.

转载和许可本文引用本文Holmlund，W.，Simkó，A.，Söderkvist，K。等人。ProstateZones–PROSTATEx数据集的前列腺区和尿道分割。

Sci Data 11, 1097 (2024). https://doi.org/10.1038/s41597-024-03945-2Download citationReceived: 10 May 2024Accepted: 26 September 2024Published: 08 October 2024DOI: https://doi.org/10.1038/s41597-024-03945-2Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Sci数据111097（2024）。https://doi.org/10.1038/s41597-024-03945-2Download引文接收日期：2024年5月10日接受日期：2024年9月26日发布日期：2024年10月8日OI：https://doi.org/10.1038/s41597-024-03945-2Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

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