Fully automated imaging protocol independent system for pituitary adenoma segmentation: a convolutional neural network - based model on sparsely annotated MRI

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00843989%3A_____%2F23%3AE0110263" target="_blank" >RIV/00843989:_____/23:E0110263 - isvavai.cz</a>

Alternative codes found

RIV/61383082:_____/23:00001305 RIV/68407700:21230/23:00366749 RIV/61988987:17110/23:A2402O0Q RIV/00216208:11110/23:10465399 RIV/00216208:11120/23:43925986

Result on the web

<a href="https://link.springer.com/article/10.1007/s10143-023-02014-3" target="_blank" >https://link.springer.com/article/10.1007/s10143-023-02014-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10143-023-02014-3" target="_blank" >10.1007/s10143-023-02014-3</a>

Result language

angličtina

Original language name

Fully automated imaging protocol independent system for pituitary adenoma segmentation: a convolutional neural network - based model on sparsely annotated MRI

Original language description

This study aims to develop a fully automated imaging protocol independent system for pituitary adenoma segmentation from magnetic resonance imaging (MRI) scans that can work without user interaction and evaluate its accuracy and utility for clinical applications. We trained two independent artificial neural networks on MRI scans of 394 patients. The scans were acquired according to various imaging protocols over the course of 11 years on 1.5T and 3T MRI systems. The segmentation model assigned a class label to each input pixel (pituitary adenoma, internal carotid artery, normal pituitary gland, background). The slice segmentation model classified slices as clinically relevant (structures of interest in slice) or irrelevant (anterior or posterior to sella turcica). We used MRI data of another 99 patients to evaluate the performance of the model during training. We validated the model on a prospective cohort of 28 patients, Dice coefficients of 0.910, 0.719, and 0.240 for tumour, internal carotid artery, and normal gland labels, respectively, were achieved. The slice selection model achieved 82.5% accuracy, 88.7% sensitivity, 76.7% specificity, and an AUC of 0.904. A human expert rated 71.4% of the segmentation results as accurate, 21.4% as slightly inaccurate, and 7.1% as coarsely inaccurate. Our model achieved good results comparable with recent works of other authors on the largest dataset to date and generalized well for various imaging protocols. We discussed future clinical applications, and their considerations. Models and frameworks for clinical use have yet to be developed and evaluated.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30224 - Radiology, nuclear medicine and medical imaging

Project

<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neurosurgical review

ISSN

0344-5607

e-ISSN

1437-2320

Volume of the periodical

46

Issue of the periodical within the volume

article 116

Country of publishing house

DE - GERMANY

Number of pages

11

Pages from-to

1-11

UT code for WoS article

000985819900001

EID of the result in the Scopus database

2-s2.0-85158846436