Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Sökning: WFRF:(Lidén Per) > Differentiation of ...

Jendeberg, Johan,1972-Örebro universitet,Institutionen för medicinska vetenskaper,Department of Radiology, Örebro University Hospital, Örebro, Sweden (författare)

Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

Artikel/kapitelEngelska2021

Förlag, utgivningsår, omfång ...

2020-02-27
Springer Berlin/Heidelberg,2021
printrdacarrier

Nummerbeteckningar

LIBRIS-ID:oai:DiVA.org:oru-80303
https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-80303URI
https://doi.org/10.1007/s00240-020-01180-zDOI

Kompletterande språkuppgifter

Språk:engelska
Sammanfattning på:engelska

Ingår i deldatabas

SwePubSwePub

Klassifikation

Ämneskategori:ref swepub-contenttype
Ämneskategori:art swepub-publicationtype

Anmärkningar

Funding Agencies:Region Örebro län OLL-684531Nyckelfonden OLL-787911
The objectives were to develop and validate a Convolutional Neural Network (CNN) using local features for differentiating distal ureteral stones from pelvic phleboliths, compare the CNN method with a semi-quantitative method and with radiologists' assessments and to evaluate whether the assessment of a calcification and its local surroundings is sufficient for discriminating ureteral stones from pelvic phleboliths in non-contrast-enhanced CT (NECT). We retrospectively included 341 consecutive patients with acute renal colic and a ureteral stone on NECT showing either a distal ureteral stone, a phlebolith or both. A 2.5-dimensional CNN (2.5D-CNN) model was used, where perpendicular axial, coronal and sagittal images through each calcification were used as input data for the CNN. The CNN was trained on 384 calcifications, and evaluated on an unseen dataset of 50 stones and 50 phleboliths. The CNN was compared to the assessment by seven radiologists who reviewed a local 5 × 5 × 5 cm image stack surrounding each calcification, and to a semi-quantitative method using cut-off values based on the attenuation and volume of the calcifications. The CNN differentiated stones and phleboliths with a sensitivity, specificity and accuracy of 94%, 90% and 92% and an AUC of 0.95. This was similar to a majority vote accuracy of 93% and significantly higher (p = 0.03) than the mean radiologist accuracy of 86%. The semi-quantitative method accuracy was 49%. In conclusion, the CNN differentiated ureteral stones from phleboliths with higher accuracy than the mean of seven radiologists' assessments using local features. However, more than local features are needed to reach optimal discrimination.

Ämnesord och genrebeteckningar

TEKNIK OCH TEKNOLOGIER Medicinteknik Medicinsk bildbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering Medical Image Processing hsv//eng
Computed tomography
Convolutional neural networks
Deep learning
Pelvic phlebolith
Ureteral calculi

Biuppslag (personer, institutioner, konferenser, titlar ...)

Thunberg, Per,1968-Örebro universitet,Institutionen för medicinska vetenskaper,Region Örebro län,Department of Medical Physics(Swepub:oru)prtg (författare)
Lidén, Mats,1976-Örebro universitet,Institutionen för medicinska vetenskaper,Region Örebro län,Department of Radiology(Swepub:oru)msld (författare)
Örebro universitetInstitutionen för medicinska vetenskaper (creator_code:org_t)

Sammanhörande titlar

Ingår i:Urolithiasis: Springer Berlin/Heidelberg49, s. 41-492194-72282194-7236

Internetlänk

Hitta via bibliotek

Urolithiasis (Sök värdpublikationen i LIBRIS)

Till lärosätets databas

Hitta mer i SwePub

Av författaren/redakt...: Jendeberg, Johan ...; Thunberg, Per, 1 ...; Lidén, Mats, 197 ...

Om ämnet

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Medicinteknik; och Medicinsk bildbe ...

Artiklar i publikationen: Urolithiasis

Av lärosätet: Örebro universitet

Sök utanför SwePub

Sök vidare i:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se