Articles producció científica> Enginyeria Informàtica i Matemàtiques

Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics

  • Dades identificatives

    Identificador: imarina:9287711
    Autors:
    Mazher, MoonaQayyum, AbdulPuig, DomenecAbdel-Nasser, Mohamed
    Resum:
    To completely comprehend neurodevelopment in healthy and congenitally abnormal fetuses, quantitative analysis of the human fetal brain is essential. This analysis requires the use of automatic multi-tissue fetal brain segmentation techniques. This paper proposes an end-to-end automatic yet effective method for a multi-tissue fetal brain segmentation model called IRMMNET. It includes a inception residual encoder block (EB) and a dense spatial attention (DSAM) block, which facilitate the extraction of multi-scale fetal-brain-tissue-relevant information from multi-view MRI images, enhance the feature reuse, and substantially reduce the number of parameters of the segmentation model. Additionally, we propose three methods for predicting gestational age (GA)-GA prediction by using a 3D autoencoder, GA prediction using radiomics features, and GA prediction using the IRMMNET segmentation model's encoder. Our experiments were performed on a dataset of 80 pathological and non-pathological magnetic resonance fetal brain volume reconstructions across a range of gestational ages (20 to 33 weeks) that were manually segmented into seven different tissue categories. The results showed that the proposed fetal brain segmentation model achieved a Dice score of 0.791 & PLUSMN;0.18, outperforming the state-of-the-art methods. The radiomics-based GA prediction methods achieved the best results (RMSE: 1.42). We also demonstrated the generalization capabilities of the proposed methods for tasks such as head and neck tumor segmentation and the prediction of patients' survival days.
  • Altres:

    Autor segons l'article: Mazher, Moona; Qayyum, Abdul; Puig, Domenec; Abdel-Nasser, Mohamed
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Mazher, Moona / Puig Valls, Domènec Savi
    Paraules clau: Prediction Multi-view segmentation Machine learning Fetal brain Fetal age prediction Deep learning
    Resum: To completely comprehend neurodevelopment in healthy and congenitally abnormal fetuses, quantitative analysis of the human fetal brain is essential. This analysis requires the use of automatic multi-tissue fetal brain segmentation techniques. This paper proposes an end-to-end automatic yet effective method for a multi-tissue fetal brain segmentation model called IRMMNET. It includes a inception residual encoder block (EB) and a dense spatial attention (DSAM) block, which facilitate the extraction of multi-scale fetal-brain-tissue-relevant information from multi-view MRI images, enhance the feature reuse, and substantially reduce the number of parameters of the segmentation model. Additionally, we propose three methods for predicting gestational age (GA)-GA prediction by using a 3D autoencoder, GA prediction using radiomics features, and GA prediction using the IRMMNET segmentation model's encoder. Our experiments were performed on a dataset of 80 pathological and non-pathological magnetic resonance fetal brain volume reconstructions across a range of gestational ages (20 to 33 weeks) that were manually segmented into seven different tissue categories. The results showed that the proposed fetal brain segmentation model achieved a Dice score of 0.791 & PLUSMN;0.18, outperforming the state-of-the-art methods. The radiomics-based GA prediction methods achieved the best results (RMSE: 1.42). We also demonstrated the generalization capabilities of the proposed methods for tasks such as head and neck tumor segmentation and the prediction of patients' survival days.
    Àrees temàtiques: Saúde coletiva Physics, multidisciplinary Physics and astronomy (miscellaneous) Physics and astronomy (all) Medicina ii Medicina i Mathematical physics Matemática / probabilidade e estatística Interdisciplinar Information systems Geociências General physics and astronomy Filosofía Engenharias iv Engenharias iii Electrical and electronic engineering Educação física Ciências biológicas i Ciência da computação Astronomia / física
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: mohamed.abdelnasser@urv.cat moona.mazher@estudiants.urv.cat domenec.puig@urv.cat
    Identificador de l'autor: 0000-0002-1074-2441 0000-0003-4444-5776 0000-0002-0562-4205
    Data d'alta del registre: 2024-10-12
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.mdpi.com/1099-4300/24/12/1708
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Entropy. 24 (12): 1708-
    Referència de l'ítem segons les normes APA: Mazher, Moona; Qayyum, Abdul; Puig, Domenec; Abdel-Nasser, Mohamed (2022). Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics. Entropy, 24(12), 1708-. DOI: 10.3390/e24121708
    DOI de l'article: 10.3390/e24121708
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2022
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Electrical and Electronic Engineering,Information Systems,Mathematical Physics,Physics and Astronomy (Miscellaneous),Physics, Multidisciplinary
    Prediction
    Multi-view segmentation
    Machine learning
    Fetal brain
    Fetal age prediction
    Deep learning
    Saúde coletiva
    Physics, multidisciplinary
    Physics and astronomy (miscellaneous)
    Physics and astronomy (all)
    Medicina ii
    Medicina i
    Mathematical physics
    Matemática / probabilidade e estatística
    Interdisciplinar
    Information systems
    Geociências
    General physics and astronomy
    Filosofía
    Engenharias iv
    Engenharias iii
    Electrical and electronic engineering
    Educação física
    Ciências biológicas i
    Ciência da computação
    Astronomia / física
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