Nakagami imaging and morphing for multiple sclerosis lesion volume estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00179906%3A_____%2F24%3A10469462" target="_blank" >RIV/00179906:_____/24:10469462 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62690094:18450/24:50020617

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=6qvDdRfWAo" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=6qvDdRfWAo</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2023.121250" target="_blank" >10.1016/j.eswa.2023.121250</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Nakagami imaging and morphing for multiple sclerosis lesion volume estimation

Popis výsledku v původním jazyce

Monitoring and tracking the size and the number of multiple sclerosis (MS) lesions is very important in clinical medicine to understand the course and estimate the progression of this demyelination disease. The lesions could be identified by the experts with brain magnetic resonance imaging (MRI) technology, especially the fluid attenuated inversion recovery sequence (FLAIR), which generates two-dimensional slices sampled from the three-dimensional space with specified slice thickness and increment values. Not every MRI scan, however, could be contiguous nor overlapping due to many reasons, to prevent from a drastic increase in the overall duration of the scans. Particularly, it is very hard to stabilize a child for hours in the same position; therefore, the specialists keep the scan procedure as short as possible, by increasing the slice thickness and more importantly, reducing the number of slices which cause some consistent gaps emerging between the slices and leading to inconclusive results. Given these facts, we propose a novel procedure to overcome this inadequacy by filling the gaps of incremental MRIs based on a Nakagami imaging and a content-based morphing method generating imaginary frames between the genuine MRI slices. Afterwards, the segmented images are reconstructed in three-dimensional space to estimate the lesion volumes for three consecutive scans of one patient. The results are greatly encouraging that we calculated 95.72% as the mean average percentage accuracy (MAPA) with 92.17% dice score (DSC%); while a little sacrifice in DSC% down to 90.35% provided us a better MAPA of 96.44%; while without morphing, the MAPA was calculated using only the binary ground truth (GT) images as 85.97%. As an expert system, the automated framework we presented would be very beneficial for volume estimations in clinics as well as visualizing the lesions and tracking the progression of MS disease.

Název v anglickém jazyce

Nakagami imaging and morphing for multiple sclerosis lesion volume estimation

Popis výsledku anglicky

Monitoring and tracking the size and the number of multiple sclerosis (MS) lesions is very important in clinical medicine to understand the course and estimate the progression of this demyelination disease. The lesions could be identified by the experts with brain magnetic resonance imaging (MRI) technology, especially the fluid attenuated inversion recovery sequence (FLAIR), which generates two-dimensional slices sampled from the three-dimensional space with specified slice thickness and increment values. Not every MRI scan, however, could be contiguous nor overlapping due to many reasons, to prevent from a drastic increase in the overall duration of the scans. Particularly, it is very hard to stabilize a child for hours in the same position; therefore, the specialists keep the scan procedure as short as possible, by increasing the slice thickness and more importantly, reducing the number of slices which cause some consistent gaps emerging between the slices and leading to inconclusive results. Given these facts, we propose a novel procedure to overcome this inadequacy by filling the gaps of incremental MRIs based on a Nakagami imaging and a content-based morphing method generating imaginary frames between the genuine MRI slices. Afterwards, the segmented images are reconstructed in three-dimensional space to estimate the lesion volumes for three consecutive scans of one patient. The results are greatly encouraging that we calculated 95.72% as the mean average percentage accuracy (MAPA) with 92.17% dice score (DSC%); while a little sacrifice in DSC% down to 90.35% provided us a better MAPA of 96.44%; while without morphing, the MAPA was calculated using only the binary ground truth (GT) images as 85.97%. As an expert system, the automated framework we presented would be very beneficial for volume estimations in clinics as well as visualizing the lesions and tracking the progression of MS disease.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30224 - Radiology, nuclear medicine and medical imaging

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

1873-6793

Svazek periodika

235

Číslo periodika v rámci svazku

JAN

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

121250

Kód UT WoS článku

001067913600001

EID výsledku v databázi Scopus

2-s2.0-85168795556