An annotated test-retest collection of prostate multiparametric MRI

Fedorov, Andriy; Schwier, Michael; Clunie, David A.; Herz, Christian; Pieper, Steve; Kikinis, Ron; Tempany, Clare M.; Fennessy, Fiona M.

doi:10.1038/sdata.2018.281

Cited by 31 publications

(37 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study used a previously published, publicly available prostate mpMRI test-retest dataset 40 composed of fifteen treatment-naïve men with biopsy-confirmed (n = 11) (using a sextant biopsy technique) or suspected (n = 4) prostate cancer (PCa). After providing informed consent, all 15 patients underwent a second MR within two weeks after the first MR, without any interim treatment 42 .…”

Section: Methodsmentioning

confidence: 99%

“…Notably, all resulting tumor ROIs used for calculating the features were smaller than 0.8 ml. Details relating to the acquisition and annotation of the dataset are described elsewhere 40,42 .…”

Section: Methodsmentioning

confidence: 99%

“…In this study we assess the repeatability of radiomics features using a publicly available dataset 40 of small prostate tumors in multiparametric prostate MR images (mpMRI). We consider all features implemented in the open source pyradiomics package 41 , which are, for the most part, implemented according to consensus definitions of the Imaging Biomarkers Standardization Initiative (IBSI) 29 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Repeatability of Multiparametric Prostate MRI Radiomics Features

Schwier

Griethuysen

Vangel

et al. 2019

Sci Rep

Self Cite

184

194

View full text Add to dashboard Cite

In this study we assessed the repeatability of radiomics features on small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI). The premise of radiomics is that quantitative image-based features can serve as biomarkers for detecting and characterizing disease. For such biomarkers to be useful, repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, and different bin widths for image discretization. Although we found many radiomics features and preprocessing combinations with high repeatability (Intraclass Correlation Coefficient > 0.85), our results indicate that overall the repeatability is highly sensitive to the processing parameters. Neither image normalization, using a variety of approaches, nor the use of pre-filtering options resulted in consistent improvements in repeatability. We urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend the use of open source implementations.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Repeatability of Multiparametric Prostate MRI Radiomics Features

Schwier

Griethuysen

Vangel

et al. 2019

Sci Rep

Self Cite

184

194

View full text Add to dashboard Cite

show abstract

“…The use of standard DICOM objects for data representation enables harmonization of the LIDC-IDRI annotations with other annotations already available in the TCIA collections (e.g., QIN-HEADNECK, 17 QIN-Prostate-Repeatability 47 and NSCLC-Radiomics 48 are just some of the examples of the collections that utilize the same type of standard objects for storing annotations and derived measurements). The practical implication of this is that the same visualization, query and processing tools can be applied to all those collections.…”

Section: Discussionmentioning

confidence: 99%

DICOM re‐encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules

Fedorov

Hancock

Clunie³

et al. 2020

Medical Physics

Self Cite

View full text Add to dashboard Cite

The dataset contains annotations for lung nodules collected by the Lung Imaging Data Consortium and Image Database Resource Initiative (LIDC) stored as standard DICOM objects. The annotations accompany a collection of computed tomography (CT) scans for over 1000 subjects annotated by multiple expert readers, and correspond to "nodules ≥ 3 mm", defined as any lesion considered to be a nodule with greatest in-plane dimension in the range 3-30 mm regardless of presumed histology. The present dataset aims to simplify reuse of the data with the readily available tools, and is targeted towards researchers interested in the analysis of lung CT images. Acquisition and validation methods: Open source tools were utilized to parse the project-specific XML representation of LIDC-IDRI annotations and save the result as standard DICOM objects. Validation procedures focused on establishing compliance of the resulting objects with the standard, consistency of the data between the DICOM and project-specific representation, and evaluating interoperability with the existing tools. Data format and usage notes: The dataset utilizes DICOM Segmentation objects for storing annotations of the lung nodules, and DICOM Structured Reporting objects for communicating qualitative evaluations (nine attributes) and quantitative measurements (three attributes) associated with the nodules. The total of 875 subjects contain 6859 nodule annotations. Clustering of the neighboring annotations resulted in 2651 distinct nodules. The data are available in TCIA at https://doi.org/10.7937/ TCIA.2018.h7umfurq. Potential applications: The standardized dataset maintains the content of the original contribution of the LIDC-IDRI consortium, and should be helpful in developing automated tools for characterization of lung lesions and image phenotyping. In addition to those properties, the representation of the present dataset makes it more FAIR (Findable, Accessible, Interoperable, Reusable) for the research community, and enables its integration with other standardized data collections.

show abstract

“…We also used an additional dataset released by the Brigham and Women's Hospital (BWH) on multi-parametric MR (mp-MR) prostate dataset [30], [31]. The BWH dataset is composed by the baseline and repeat prostate MR exams for 15 subjects.…”

Section: Methodsmentioning

confidence: 99%

Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

Zhu

Yan

2020

IEEE Trans. Med. Imaging

164

View full text Add to dashboard Cite

Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segmentation from 3D MR images faces several challenges. The lack of clear edge between the prostate and other anatomical structures makes it challenging to accurately extract the boundaries. The complex background texture and large variation in size, shape and intensity distribution of the prostate itself make segmentation even further complicated. Recently, as deep learning, especially convolutional neural networks (CNNs), emerging as the best performed methods for medical image segmentation, the difficulty in obtaining large number of annotated medical images for training CNNs has become much more pronounced than ever. Since large-scale dataset is one of the critical components for the success of deep learning, lack of sufficient training data makes it difficult to fully train complex CNNs. To tackle the above challenges, in this paper, we propose a boundary-weighted domain adaptive neural network (BOWDA-Net). To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss is proposed. Furthermore, an advanced boundary-weighted transfer leaning approach is introduced to address the problem of small medical imaging datasets. We evaluate our proposed model on three different MR prostate datasets. The experimental results demonstrate that the proposed model is more sensitive to object boundaries and outperformed other state-of-the-art methods.

show abstract

An annotated test-retest collection of prostate multiparametric MRI

Cited by 31 publications

References 44 publications

Repeatability of Multiparametric Prostate MRI Radiomics Features

Repeatability of Multiparametric Prostate MRI Radiomics Features

DICOM re‐encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules

Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

Contact Info

Product

Resources

About