Discrimination between distal ureteral stones and pelvic phleboliths in CT using a deep neural network: more than local features needed

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

Sökning: WFRF:(Lidén Per) > Discrimination betw...

LIBRIS Formathandbok (Information om MARC21)

Fältnamn	Indikatorer	Metadata
000		03394naa a2200289 4500
001		oai:DiVA.org:oru-67372
003		SwePub
008		180620s2018 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-673722 URI
040		a (SwePub)oru
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a kon2 swepub-publicationtype
100	1	a Lidén, Mats,d 1976-u Örebro universitet,Institutionen för medicinska vetenskaper4 aut0 (Swepub:oru)msld
245	1 0	a Discrimination between distal ureteral stones and pelvic phleboliths in CT using a deep neural network :b more than local features needed
264	1	c 2018
338		a print2 rdacarrier
520		a Purpose: To develop a deep learning method for assisting radiologists in the discrimination between distal ureteral stones and pelvic phleboliths in thin slice CT images, and to evaluate whether this differentiation is possible using only local features.Methods and materials: A limited field-of-view image data bank was retrospectively created, consisting of 5x5x5 cm selections from 1 mm thick unenhanced CT images centered around 218 pelvis phleboliths and 267 distal ureteral stones in 336 patients. 50 stones and 50 phleboliths formed a validation cohort and the remainder a training cohort. Ground truth was established by a radiologist using the complete CT examination during inclusion.The limited field-of-view CT stacks were independently reviewed and classified as containing a distal ureteral stone or a phlebolith by seven radiologists. Each cropped stack consisted of 50 slices (5x5 cm field-of-view) and was displayed in a standard PACS reading environment. A convolutional neural network using three perpendicular images (2.5D-CNN) from the limited field-of-view CT stacks was trained for classification.Results: The 2.5D-CNN obtained 89% accuracy (95% confidence interval 81%-94%) for the classification in the unseen validation cohort while the accuracy of radiologists reviewing the same cohort was 86% (range 76%-91%). There was no statistically significant difference between 2.5D-CNN and radiologists.Conclusion: The 2.5D-CNN achieved radiologist level classification accuracy between distal ureteral stones and pelvic phleboliths when only using the local features. The mean accuracy of 86% for radiologists using limited field-of-view indicates that distant anatomical information that helps identifying the ureter’s course is needed.
650	7	a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Radiologi och bildbehandling0 (SwePub)302082 hsv//swe
650	7	a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Radiology, Nuclear Medicine and Medical Imaging0 (SwePub)302082 hsv//eng
700	1	a Jendeberg, Johan,d 1972-u Örebro universitet,Institutionen för medicinska vetenskaper4 aut0 (Swepub:oru)jjg
700	1	a Längkvist, Martin,d 1983-u Örebro universitet,Institutionen för naturvetenskap och teknik4 aut0 (Swepub:oru)milt
700	1	a Loutfi, Amy,d 1978-u Örebro universitet,Institutionen för naturvetenskap och teknik4 aut0 (Swepub:oru)ali
700	1	a Thunberg, Per,d 1968-u Örebro universitet,Institutionen för medicinska vetenskaper4 aut0 (Swepub:oru)prtg
710	2	a Örebro universitetb Institutionen för medicinska vetenskaper4 org
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-67372

Till lärosätets databas

Hitta mer i SwePub

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

Om ämnet

MEDICIN OCH HÄLSOVETENSKAP: MEDICIN OCH HÄLS ...; och Klinisk medicin; och Radiologi och bi ...

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