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

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30224 - Radiology, nuclear medicine and medical imaging

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Neurosurgical review

ISSN

0344-5607

e-ISSN

1437-2320

Svazek periodika

46

Číslo periodika v rámci svazku

article 116

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

11

Strana od-do

1-11

Kód UT WoS článku

000985819900001

EID výsledku v databázi Scopus

2-s2.0-85158846436