Curated benchmark dataset for ultrasound based breast lesion analysis.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
31 Jan 2024
31 Jan 2024
Historique:
received:
16
06
2023
accepted:
17
01
2024
medline:
1
2
2024
pubmed:
1
2
2024
entrez:
31
1
2024
Statut:
epublish
Résumé
A new detailed dataset of breast ultrasound scans (BrEaST) containing images of benign and malignant lesions as well as normal tissue examples, is presented. The dataset consists of 256 breast scans collected from 256 patients. Each scan was manually annotated and labeled by a radiologist experienced in breast ultrasound examination. In particular, each tumor was identified in the image using a freehand annotation and labeled according to BIRADS features and lexicon. The histopathological classification of the tumor was also provided for patients who underwent a biopsy. The BrEaST dataset is the first breast ultrasound dataset containing patient-level labels, image-level annotations, and tumor-level labels with all cases confirmed by follow-up care or core needle biopsy result. To enable research into breast disease detection, tumor segmentation and classification, the BrEaST dataset is made publicly available with the CC-BY 4.0 license.
Identifiants
pubmed: 38297002
doi: 10.1038/s41597-024-02984-z
pii: 10.1038/s41597-024-02984-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
148Informations de copyright
© 2024. The Author(s).
Références
Sung, H. et al. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians 71, 209–249, https://doi.org/10.3322/caac.21660 (2021).
doi: 10.3322/caac.21660
pubmed: 33538338
Madjar, H., Mendelson, E. & Jellins, J. The Practice of Breast Ultrasound: Techniques, Findings, Differential Diagnosis. Thieme Publishers Series (John Wiley & Sons, Limited, 2008).
D’Orsi, C., Sickles, E., Mendelson, E. & Morris, E. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System (American College of Radiology, Reston, VA, 2013).
Shimamoto, K. et al. Interobserver agreement in sonographic diagnosis of breast tumors. European Journal of Ultrasound 8, 25–31, https://doi.org/10.1016/S0929-8266(98)00047-0 (1998).
doi: 10.1016/S0929-8266(98)00047-0
pubmed: 9795006
Schwab, F. et al. Inter- and intra-observer agreement in ultrasound bi-rads classification and real-time elastography tsukuba score assessment of breast lesions. Ultrasound in Medicine & Biology 42, 2622–2629, https://doi.org/10.1016/j.ultrasmedbio.2016.06.017 (2016).
doi: 10.1016/j.ultrasmedbio.2016.06.017
Nicosia, L. et al. Automatic breast ultrasound: State of the art and future perspectives. ecancermedicalscience 14, https://doi.org/10.3332/ecancer.2020.1062 (2020).
Xue, C. et al. Global guidance network for breast lesion segmentation in ultrasound images. Medical Image Analysis 70, 101989, https://doi.org/10.1016/j.media.2021.101989 (2021).
doi: 10.1016/j.media.2021.101989
pubmed: 33640719
Shen, X. et al. Lesion segmentation in breast ultrasound images using the optimized marked watershed method. BioMedical Engineering OnLine 20, https://doi.org/10.1186/s12938-021-00891-7 (2021).
Shia, W.-C. & Chen, D.-R. Classification of malignant tumors in breast ultrasound using a pretrained deep residual network model and support vector machine. Computerized Medical Imaging and Graphics 87, 101829, https://doi.org/10.1016/j.compmedimag.2020.101829 (2021).
doi: 10.1016/j.compmedimag.2020.101829
pubmed: 33302247
Shi, X., Cheng, H., Hu, L., Ju, W. & Tian, J. Detection and classification of masses in breast ultrasound images. Digital Signal Processing 20, 824–836, https://doi.org/10.1016/j.dsp.2009.10.010 (2010).
doi: 10.1016/j.dsp.2009.10.010
Butcher, B. & Smith, B. J. Feature engineering and selection: A practical approach for predictive models. The American Statistician 74, 308–309, https://doi.org/10.1080/00031305.2020.1790217 (2020).
doi: 10.1080/00031305.2020.1790217
Nagendran, M. et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ m689, https://doi.org/10.1136/bmj.m689 (2020).
Ouyang, C. et al. Causality-Inspired Single-Source Domain Generalization for Medical Image Segmentation. IEEE Transactions on Medical Imaging 42, 1095–1106, https://doi.org/10.1109/TMI.2022.3224067 (2023).
doi: 10.1109/TMI.2022.3224067
pubmed: 36417741
Piotrzkowska-Wróblewska, H., Dobruch-Sobczak, K., Byra, M. & Nowicki, A. Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions. Medical Physics 44, 6105–6109, https://doi.org/10.1002/mp.12538 (2017).
doi: 10.1002/mp.12538
pubmed: 28859252
Rodtook, A., Kirimasthong, K., Lohitvisate, W. & Makhanov, S. S. Automatic initialization of active contours and level set method in ultrasound images of breast abnormalities. Pattern Recognition 79, 172–182, https://doi.org/10.1016/j.patcog.2018.01.032 (2018).
doi: 10.1016/j.patcog.2018.01.032
Yap, M. H. et al. Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics 22, 1218–1226, https://doi.org/10.1109/JBHI.2017.2731873 (2018).
doi: 10.1109/JBHI.2017.2731873
pubmed: 28796627
Lin, Z. et al. A new dataset and baseline model for breast lesion detection in ultrasound videos. In Wang, L., Dou, Q., Fletcher, P. T., Speidel, S. & Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, 614–623, https://doi.org/10.1007/978-3-031-16437-8_59 (Springer Nature Switzerland, Cham, 2022).
Al-Dhabyani, W., Gomaa, M., Khaled, H. & Fahmy, A. Dataset of breast ultrasound images. Data in Brief 28, 104863, https://doi.org/10.1016/j.dib.2019.104863 (2020).
doi: 10.1016/j.dib.2019.104863
pubmed: 31867417
Abbasian Ardakani, A., Mohammadi, A., Mirza-Aghazadeh-Attari, M. & Acharya, U. R. An open-access breast lesion ultrasound image database: Applicable in artificial intelligence studies. Computers in Biology and Medicine 152, 106438, https://doi.org/10.1016/j.compbiomed.2022.106438 (2023).
doi: 10.1016/j.compbiomed.2022.106438
pubmed: 36535208
Pawłwska, A., Karwat, P. & Żołek, N. Letter to the editor. re: “[dataset of breast ultrasound images by w. al-dhabyani, m. gomaa, h. khaled & a. fahmy, data in brief, 2020, 28, 104863]”. Data in Brief 48, 109247, https://doi.org/10.1016/j.dib.2023.109247 (2023).
doi: 10.1016/j.dib.2023.109247
Pawłwska, A. et al. A curated benchmark dataset for ultrasound based breast lesion analysis (breast-lesions-usg) (version 1), The Cancer Imaging Archive, https://doi.org/10.7937/9wkk-q141 (2024).
World Health Organization. The ICD-10 classification of mental and behavioural disorders (World Health Organization, 1993).
WHO Classification of Tumours Editorial Board. WHO Classification of Tumours: Breast Tumours, vol. 2 of World Health Organization classification of tumours 5 edn (IARC, 2019).
BrEaST dataset web viewer, https://best.ippt.pan.pl/datasets/breast .
Marinovich, M. et al. Meta-Analysis of Magnetic Resonance Imaging in Detecting Residual Breast Cancer After Neoadjuvant Therapy. Journal of the National Cancer Institute 105, 321–333, https://doi.org/10.1093/jnci/djs528 (2013).
doi: 10.1093/jnci/djs528
pubmed: 23297042
Rosen, R. & Sapra, A. TNM Classification (StatPearls Publishing, 2020).
de Carvalho, J. D., Guliato, D., Santiago, S. A. & Rangayyan, R. M. Polygonal Modeling of Contours Using the Turning Angle Function. In 2007 Canadian Conference on Electrical and Computer Engineering, 1090–1093, https://doi.org/10.1109/CCECE.2007.278 (IEEE, 2007).
BrEaST dataset import scripts. GitHub repository https://github.com/best-ippt-pan-pl/BrEaST/ (2024).
cornerstone3D library. GitHub repository https://github.com/cornerstonejs/cornerstone3D (2022).
dicomParser library. GitHub repository https://github.com/cornerstonejs/dicomParser (2022).
Nanodicom library. GitHub repository https://github.com/nanodocumet/Nanodicom (2022).
markerjs2 library. GitHub repository https://github.com/ailon/markerjs2 (2022).