{"id":184,"date":"2016-03-15T23:49:11","date_gmt":"2016-03-15T22:49:11","guid":{"rendered":"http:\/\/www.imagenglab.com\/newsite\/?page_id=184"},"modified":"2017-11-07T17:30:44","modified_gmt":"2017-11-07T16:30:44","slug":"pddca","status":"publish","type":"page","link":"https:\/\/www.imagenglab.com\/newsite\/pddca\/","title":{"rendered":"PDDCA"},"content":{"rendered":"

PDDCA<\/strong><\/p>\n

A Public Domain Database for Computational Anatomy<\/strong><\/p>\n

version 1.4.1<\/strong><\/p>\n

This database is provided and maintained by Dr. Gregory C Sharp (Harvard Medical School – MGH, Boston) and his group.<\/p>\n

The data here provided have been used for the “Head and Neck Auto Segmentation MICCAI Challenge (2015)”.
\nTo cite the challenge or the data, please refer to:
\nRaudaschl, P. F., Zaffino, P., Sharp, G. C., Spadea, M. F., Chen, A., Dawant, B. M., … & Jung, F. (2017).
\nEvaluation of segmentation methods on head and neck CT: Auto\u2010segmentation challenge 2015.
\nMedical Physics, 44(5), 2020-2036.<\/p>\n

PDDCA version 1.4.1 comprises 48 patient CT images from the Radiation Therapy Oncology Group (RTOG) 0522 study (a multi-institutional clinical trial led by Dr Kian Ang), together with manual segmentation of left and right parotid glands, brainstem, optic chiasm, optic nerves (both left and right), mandible, submandibular glands (both left and right) and manual identification of bony landmarks.
\nWe give this data to the community in the hopes that it will be helpful. Any errors in delineation and markup are ours, and are not the fault of participating doctors.
\nPlease see pddca.odt for complete information.
\nFor practical reasons the database is split in 3 zipped files.
\nPart 3 contains images with some of the above listed structures missing.<\/p>\n

 <\/p>\n

PDDCA description<\/a><\/p>\n

PDDCA part 1<\/a><\/p>\n

PDDCA part 2<\/a><\/p>\n

PDDCA part 3<\/a><\/p>\n

Older versions (can contain bugged data):
\nv 1.4: \u00a0 \u00a0 \u00a0 \u00a0PDDCA-1.4_part1<\/a>\u00a0 \u00a0 \u00a0 \u00a0\u00a0PDDCA-1.4_part2<\/a>\u00a0 \u00a0 \u00a0 \u00a0\u00a0PDDCA-1.4_part3<\/a>
\nv 1.3.1: \u00a0\u00a0PDDCA-1.3.1_part1<\/a>\u00a0 \u00a0\u00a0PDDCA-1.3.1_part2<\/a>\u00a0 \u00a0\u00a0PDDCA-1.3.1_part3<\/a>
\nv 1.3: \u00a0 \u00a0 \u00a0\u00a0PDDCA-1.3_part1<\/a>\u00a0 \u00a0 \u00a0 \u00a0\u00a0PDDCA-1.3_part2<\/a>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0PDDCA-1.3_part3<\/a>
\nv 1.2: \u00a0 \u00a0 \u00a0\u00a0PDDCA-1.2_part1<\/a>\u00a0 \u00a0 \u00a0 \u00a0\u00a0PDDCA-1.2_part2<\/a>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0PDDCA-1.2_part3<\/a>
\nv 1.1: \u00a0 \u00a0 \u00a0\u00a0PDDCA-1.1_part1<\/a>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0PDDCA-1.1_part2<\/a>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0PDDCA-1.1_part3<\/a>\u00a0 \u00a0 \u00a0 \u00a0\u00a0PDDCA-1.1_part4<\/a><\/p>\n

 <\/p>\n

PROBABILISTIC HEAD AND NECK ATLAS<\/strong><\/p>\n

version 1.2<\/strong><\/p>\n

Using the PDDCA dataset Dr. Kanglin Chen (Fraunhofer Institute for Medical Image Computing MEVIS) has built the probabilistic atlas linked below.
\nThe atlas, in addition to the mean anatomical volume, includes also the geometrical transformations, five bone landmarks and the probability maps of left\/right parotids and of the brainstem.<\/p>\n

The package is freely downlodable.<\/p>\n

For further information please contact the author.<\/p>\n

Head and neck probabilistic atlas<\/a><\/p>\n

Older versions(can contain bugged data):
\nv 1.1: \u00a0Head and neck probabilistic atlas v1.1<\/a>
\nv 1.0: \u00a0Head and neck probabilistic atlas v1.0<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

PDDCA A Public Domain Database for Computational Anatomy version 1.4.1 This database is provided and maintained by Dr. Gregory C Sharp (Harvard Medical School – MGH, Boston) and his group. The data here provided have been used for the “Head and Neck Auto Segmentation MICCAI Challenge (2015)”. To cite the challenge or the data, please Read more about PDDCA<\/span>[…]<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"spay_email":""},"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/P9htfR-2Y","jetpack_likes_enabled":true,"jetpack-related-posts":[{"id":100,"url":"https:\/\/www.imagenglab.com\/newsite\/research\/mabs\/","url_meta":{"origin":184,"position":0},"title":"MABS","date":"14 March 2016","format":false,"excerpt":"Multi Atlas Based Segmentation In collaboration with Dr. Gregory C. Sharp, Radiation Oncology Department, Massachusetts General Hospital - Harvard Medical School, Boston, USA. Dr. Karl Fritscher,\u00a0Department for Biomedical Image Analysis, UMIT, Austria. Multi Atlas Based Segmentation (MABS) is a methodology for automatic image segmentation that\u00a0is proving to be able to\u2026","rel":"","context":"Similar post","img":{"alt_text":"MABS2","src":"https:\/\/i1.wp.com\/www.imagenglab.com\/newsite\/wp-content\/uploads\/2016\/03\/MABS2.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":328,"url":"https:\/\/www.imagenglab.com\/newsite\/paolo_zaffino\/","url_meta":{"origin":184,"position":1},"title":"Paolo Zaffino","date":"17 April 2016","format":false,"excerpt":"Contact: e-mail: p.zaffino at imagenglab.com Paolo received his master degree cum laude in Biomedical Engineer from Magna Graecia University of Catanzaro (Italy) in 2011. After that, in the same University, he got a PhD in Bioengineering and, in 2015, he become a PostDoc and contractor Professor. He was also a\u2026","rel":"","context":"Similar post","img":{"alt_text":"","src":"https:\/\/i1.wp.com\/www.imagenglab.com\/newsite\/wp-content\/uploads\/2016\/03\/Paolo_check-e1457046740751-290x300.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":234,"url":"https:\/\/www.imagenglab.com\/newsite\/education\/alumni\/","url_meta":{"origin":184,"position":2},"title":"Alumni","date":"24 March 2016","format":false,"excerpt":"\u00a0 A.A. 2014\/2015 \u00a0Marzia Restuccia Being awarded with an Erasmus Placement travel grant, Marzia spent 3 months at CIBM (C\u00e8ntre d'Imagerie Biomedicale) in Lausanne (Switzerland). There, she learnt fMRI techniques, taking part in experimental acquisitions on phantoms and voluntary subjects. She continued her\u00a0work and her BSc work focusing on the\u2026","rel":"","context":"Similar post","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":119,"url":"https:\/\/www.imagenglab.com\/newsite\/research\/radiotherapy-of-lymphoma\/","url_meta":{"origin":184,"position":3},"title":"Radiotherapy of Lymphoma","date":"14 March 2016","format":false,"excerpt":"automatic contour delineation In collaboration with the Radiotherapy Division of the European Institute of Oncology (Milan) Cart Laboratory of Politecnico di Milano University \u00a0 Radiation therapy is one of the two steps for common treatment of head and neck Hodgkin\/non\u00ad Hodgkin lymphoma. Unfortunately, in this scenario, clear and accurate localization\u2026","rel":"","context":"Similar post","img":{"alt_text":"lymphoma","src":"https:\/\/i2.wp.com\/www.imagenglab.com\/newsite\/wp-content\/uploads\/2016\/03\/lymphoma.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1073,"url":"https:\/\/www.imagenglab.com\/newsite\/covid-19\/","url_meta":{"origin":184,"position":4},"title":"COVID-19","date":"13 October 2020","format":false,"excerpt":"IMPORTANT: ON SEPTEMBER 2021 THE ARCHIVE WAS ENRICHED WITH 31 NEW PATIENTS AND ICU INFORMATION. From this webpage it is possible downloading COVID-19 CT dataset describerd at\"Zaffino P, Marzullo A, Moccia S, Calimeri F, De Momi E, Bertucci B, Arcuri PP, Spadea MF.An Open-Source COVID-19 CT Dataset with Automatic Lung\u2026","rel":"","context":"Similar post","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/www.imagenglab.com\/newsite\/wp-json\/wp\/v2\/pages\/184"}],"collection":[{"href":"https:\/\/www.imagenglab.com\/newsite\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.imagenglab.com\/newsite\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.imagenglab.com\/newsite\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.imagenglab.com\/newsite\/wp-json\/wp\/v2\/comments?post=184"}],"version-history":[{"count":14,"href":"https:\/\/www.imagenglab.com\/newsite\/wp-json\/wp\/v2\/pages\/184\/revisions"}],"predecessor-version":[{"id":744,"href":"https:\/\/www.imagenglab.com\/newsite\/wp-json\/wp\/v2\/pages\/184\/revisions\/744"}],"wp:attachment":[{"href":"https:\/\/www.imagenglab.com\/newsite\/wp-json\/wp\/v2\/media?parent=184"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}