Review of segmenting and detecting abnormalities in infant brain MRI

Kavya N. P.; Yashasvi B. N.

Keywords:

Magnetic Resonance Imaging (MRI)

Deep Learning Techniques

Abstract

An infant’s brain MRI has appeared to be a critical diagnostic approach for detecting and understanding early neurological abnormalities. This survey explores the landscape of existing methodologies and presents a comprehensive overview of novel approaches for abnormality identification in infants’ brain MR images. The vulnerability of the developing brain necessitates specialized techniques that go beyond traditional adult brain MRI analysis. The survey begins by reviewing the advanced approaches for identifying abnormalities in infant brain MR images, focusing on challenges such as the dynamic nature of the evolving brain, variations in the quality of images, and the need for accurate and early detection of abnormalities. It then delves into the diverse range of image processing machine learning (ML) and deep learning (DL) techniques that have been employed in recent years. By synthesizing the current knowledge and identifying gaps in the literature, this study aims to inspire and guide future research toward more effective and reliable methods for detecting abnormalities in infant brain MR images.
References
1. Age terminology during the perinatal period, American Academy of Pediatrics.
2. A. Alansary, M. Ismail, A. Soliman, F. Khalifa, M. Nitzken, et al., Infant brain extraction in T1-weighted MR images using BET and refinement using LCDG and MGRF models, IEEE J. Biomed. Health Inf. (2015), http://dx.doi.org/ 10.1109/JBHI.2015.2415477.
3. M. Altaye, S.K. Holland, M. Wilke, C. Gaser, Infant brain probability templates for MRI segmentation and normalization, NeuroImage 43 (2008) 721–730.
4. P. Anbeek, K.L. Vincken, F. Groenendaal, A. Koeman, M.J.P. Osch, J. Grond, Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging, Pediatr. Res. 63 (2) (2008) 158–163.
5. P. Anbeek, I. Išgum, B.J.M. Kooij, C.P. Mol, K.J. Kersbergen, et al., Automatic segmentation of eight tissue classes in neonatal brain MRI, PLoS ONE 8 (12) (2013).
6. J. Ashburner, K.J. Friston, Unified segmentation, NeuroImage 26 (2005) 839–851.
7. M.S. Atkins, B. Mackiewich, Fully automatic segmentation of the brain in MRI, IEEE Trans. Med. Imaging 17 (1998) 98–107.
8. A.J. Barkovich, MR of the normal neonatal brain: assessment of deep structures, Am. J. Neuroradiol. 19 (1998) 1397–1403.
9. A.J. Barkovich, Concepts of myelin and myelination in neuroradiology, Am. J. Neuroradiol. 21 (2000) 1099–1109.
10. Battin M., Rutherford M.A., Magnetic resonance imaging of the brain in preterm infants: 24 weeks’ gestation to term, in: M.A. Rutherford (Ed.), MRI of the Neonatal Brain.
11. B. Belaroussi, J. Milles, S. Carme, Y.M. Zhu, H.B Cattin, Intensity nonuniformity correction in MRI: existing methods and their validation, Med. Image Anal. 10 (2006) 234–246.
12. S. Beucher, F. Meyer, The morphological approach to segmentation: the watershed transformation, in: E.R. Dougherty (Ed.), Mathematical Morphology in Image Processing, CRC Press, 1993, pp. 433–481.
13. S.T. Brady, A.S. Witt, L.L. Kirkpatrick, S.M. de Waegh, C. Readhead, et al., Formation of compact myelin is required for maturation of the axonal cytoskeleton, J. Neurosci. 19 (1999) 7278–7288.
14. Jaware, T. H., Khanchandani, K. B., & Kalal, D. (2019). An atlas-free newborn brain image segmentation and classification scheme based on Som-DCNN with sparse auto encoder. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 8(1), 49– 64.
15. Bui, T. D., Wang, L., Lin, W., Li, G., & Shen, D. 6-month infant brain MRI segmentation guided by 24-month data using cycle-consistent adversarial networks. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).
16. Zeng, Z., Zhao, T., Sun, L., Zhang, Y., Xia, M., Liao, X., Zhang, J., Shen, D., Wang, L., & He, Y. (2021, January 1).3D-masnet: 3D mixed-scale asymmetric convolutional segmentation network for 6-month-old Infant Brain Mr Images. bioRxiv. March 7, 2022.
17. Yue Sun, Kun Gao, Sijie Niu, Weili Lin, Gang Li, Li Wang, The UNC/UMN Baby Connectome Project Consortium,. (2020, October 4). Semi-supervised transfer learning for infant cerebellum tissue segmentation. SpringerLink. March 7, 2022.
18. Tushar Jaware, Kamlesh Khanchandani & Ravindra Badgujar (2020) A novel hybrid atlas-free hierarchical graphbased segmentation of newborn brain MRI using wavelet filter banks, International Journal of Neuroscience, 130:5, 499-514.
19. B. Surányi, L. Kovacs and L. Szilagyi, "Segmentation of Brain Tissues from Infant MRI Records Using Machine Learning Techniques," 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), 2021, pp. 000455-000460.
20. Sadegh Pasban,Sajad Mohamadzadeh,Javad ZeraatkarMoghaddam,Amir Keivan Shafiei,Infant brain segmentation based on a combination of VGG-16 and U-Net deep neural networks ,IET image process. 2020;14(17):4756-65, DOI: 10.1049/iet-ipr.2020.0469.
21. Zhou, H., Zhang, Y., Huang, D., Li, L. (2013). Semi-supervised Learning with Transfer Learning. In: Sun, M., Zhang, M., Lin, D., Wang, H. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2013 2013. Lecture Notes in Computer Science (), vol 8202. Springer, Berlin, Heidelberg. 11.
22. Tushar H. Jaware and K. B. Khanchandani and Durgeshwari Kalal,An atlas-free newborn brain image segmentation and classification scheme based on SOM-DCNN with sparse auto encoder,Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol 8, pages 49-64, 2020, Taylor & Francis.
23. Patil Vinodkumar Ramesh1 , Jaware Tushar Hrishikesh2 , Manisha S. Patil3, “Infant’s MRI Brain Tissue Segmentation using Integrated CNN Feature Extractor and Random Forest”, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 11 Issue: 1s DOI: https://doi.org/10.17762/ijritcc.v11i1s.6002 Article Received: 27 November 2022 Revised: 20 December 2022 Accepted: 08 January 2023
24. Jad Dino Raad1 , Ratna Babu Chinnam1 , SuzanArslanturk 2*, SidharthaTan3 , Jeong Won Jeong3 & Swati Mody4, “Unsupervised abnormality detection in neonatal MRI brain scans using deep learning”,
25. Sumitra V, Latha P, Sasikala, Hedayuth Basha, Lalitha Sudha,R.Gnanavel. (2022). Infant Brain Mri Abnormalities Detection Using Deep Learning. Computer Integrated Manufacturing Systems, 28(12), 663–678. Retrieved from http://cims-journal.com/index.php/CN/article/view/450
26. Khalili, N., Lessmann, N., Turk, E., Claessens, N., de Heus, R., Kolk, T., Viergever, M.A., Benders, M.J., and Išgum, I. Automatic Brain Tissue Segmentation in Fetal MRI using Convolutional Neural Networks. Magnetic resonance imaging, vol. 64, pp. 77-89, 2019.
27. Kornilov, A., Safonov, I., and Yakimchuk, I. A Review of Watershed Implementations for Segmentation of Volumetric Images. Journal of Imaging, vol. 8, no. 5, pp. 127, 2022
28. N. Suresh Kumar* and Amit Kumar Goel, “Detection, Localization and Classification of Fetal Brain Abnormalities using YOLO v4 Architecture”,International Journal of Performability Engineering, vol. 18, no. 10, October 2022, pp. 720-729.
29. A.A. Movassagh, J.A. Alzubi, M. Gheisari, M. Rahimi, S. Mohan, A.A. Abbasi, N. Nabipour, Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model, J. Ambient Intell. Hum. Comput. 14 (2021) 6017–6025.
30. Jafar A. Alzubi, Balasubramaniyan Bharathikannan, Sudeep Tanwar, Ramachandran Manikandand, Ashish Khanna, Chandrasekar Thaventhiran, Boosted neural network ensemble classification for lung cancer disease diagnosis, Appl. Soft Comput. 80 (July 2019) 579–591.
31. O. Attallah, H. Gadelkarim, M.A. Sharkas, Detecting and classifying fetal brain abnormalities using machine learning techniques, in: 2018 17th IEEE International Conference on Machine Learning and Applications, ICMLA, 2018, pp. 1371–1376.
32. B.J. Erickson, P. Korfiatis, Z. Akkus, T.L. Kline, Machine learning for medical imaging, Radiographics 37 (2) (2017) 505.
33. G. Gandolfi Colleoni, E. Contro, A. Carletti, T. Ghi, G. Campobasso, G. Rembouskos, G. Volpe, G. Pilu, P. Volpe, Prenatal diagnosis and outcome of fetal posterior fossa fluid collections, Ultrasound Obstet. Gynecol. 39 (6) (2012) 625–631.
34. E. Katorza, I. Gat, N. Duvdevani, N. Meller, N. Pardo, E. Barzilay, R. Achiron, Fetal brain anomalies detection during the first trimester: expanding the scope of antenatal sonography, J. Matern. Fetal Neonatal Med. 31 (4) (2018) 506–512.
35. N. Khalili, N. Lessmann, E. Turk, N. Claessens, R. de Heus, T. Kolk, M.A. Viergever, M.J.N.L. Benders, I. Iˇsgum, Automatic brain tissue segmentation in fetal MRI using convolutional neural networks, Magn. Reson. Imag. 64 (2019) 77–89, https://doi. org/10.1016/j.mri.2019.05.020.
36. D.J. Lowsky, Y. Ding, D.K.K. Lee, C.E. McCulloch, L.F. Ross, J.R. Thistlethwaite, S. A. Zenios, AK-nearest neighbors survival probability prediction method, Stat. Med. 32 (12) (2013) 2062–2069.
37. M. Sanz-Cort´es, F. Figueras, E. Bonet-Carne, N. Padilla, V. Tenorio, N. Bargallo, I. Amat-Roldan, E. Gratacos, ´ Fetal brain MRI texture analysis identifies different microstructural patterns inadequate and small for gestational age fetuses at term, Fetal Diagn. Ther. 33 (2) (2013) 122–129.
38. M. Sanz-Cortes, G.A. Ratta, F. Figueras, E. Bonet-Carne, N. Padilla, A. Arranz, N. Bargallo, E. Gratacos, Automatic quantitative MRI texture analysis in small-forgestational-age fetuses discriminates abnormal neonatal neurobehavior, PLoS One 8 (7) (2013), e69595.
39. M. Priya, M. Nandhini, “Detection of fetal brain abnormalities using data augmentation and convolutional neural network in internet of things”, Available online 14 June 2023 2665-9174/© 2023 Published by Elsevier Ltd.
40. V. Anitha S. Murugavalli, “Brain Tumour Classification Using Two-Tier Classifier With Adaptive Segmentation Technique. 2016
41. G Ball, “Machine-Learning To Characterise Neonatal Functional Connectivity In The Preterm Brain”, Neuroimage 2016
42. Bradley J.Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy L. Klinefeb, “Machine Learning For Medical Imaging”, 2017
43. Christopher D. Smyser, Nico U.F. Dosenbach, [...], And Jeffrey J. Neil “Prediction Of Brain Maturity In Infants Using Machine-Learning Algorithms”, 2016
44. Derek Bradley, Gerhard Roth , “Adaptive Thresholding Using The Integral Image”, 30 Jan 2011.
45. Eldad Katorza Et Al. J Matern Fetal Neonatal Med , “Fetal Brain Anomalies Detection During The First Trimester: Expanding The Scope Of Antenatal Sonography”, 2018.
46. Magdalena Sanz-Cortes Et Al. Plos One , “Automatic Quantitative Mri Texture Analysis In Small-For-Gestational-Age Fetuses Discriminates Abnormal Neonatal Neurobehavior “, 2013
47. Mohammad Havaeia,1, Axel Davyb, David Warde-Farleyc, Antoine Biardc,D, Aaron Courvillec, Yoshua Bengioc, Chris Palc,E,Pierre-Marc Jodoina, Hugo Larochellea,F “Brain Tumor Segmentation With Deep Neural Networks” [Received 27 April 2015, Revised 2 March 2016, Accepted 11 May 2016, Available Online 19 May 2016.]
48. Yan Jin Et Al. Hum , “Identification Of Infants At High-Risk For Autism Spectrum Disorder Using Multiparameter Multiscale White Matter Connectivity Networks”, [Brain Mapp2015 Dec].

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Kavya N. P., & Yashasvi B. N. (2025). Review of segmenting and detecting abnormalities in infant brain MRI. Multidisciplinary Reviews, (| Accepted Articles). Retrieved from https://malque.pub/ojs/index.php/mr/article/view/7159

[1] Age terminology during the perinatal period, American Academy of Pediatrics.

[2] A. Alansary, M. Ismail, A. Soliman, F. Khalifa, M. Nitzken, et al., Infant brain extraction in T1-weighted MR images using BET and refinement using LCDG and MGRF models, IEEE J. Biomed. Health Inf. (2015), http://dx.doi.org/ 10.1109/JBHI.2015.2415477.

[3] M. Altaye, S.K. Holland, M. Wilke, C. Gaser, Infant brain probability templates for MRI segmentation and normalization, NeuroImage 43 (2008) 721–730.

[4] P. Anbeek, K.L. Vincken, F. Groenendaal, A. Koeman, M.J.P. Osch, J. Grond, Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging, Pediatr. Res. 63 (2) (2008) 158–163.

[5] P. Anbeek, I. Išgum, B.J.M. Kooij, C.P. Mol, K.J. Kersbergen, et al., Automatic segmentation of eight tissue classes in neonatal brain MRI, PLoS ONE 8 (12) (2013).

[6] J. Ashburner, K.J. Friston, Unified segmentation, NeuroImage 26 (2005) 839–851.

[7] M.S. Atkins, B. Mackiewich, Fully automatic segmentation of the brain in MRI, IEEE Trans. Med. Imaging 17 (1998) 98–107.

[8] A.J. Barkovich, MR of the normal neonatal brain: assessment of deep structures, Am. J. Neuroradiol. 19 (1998) 1397–1403.

[9] A.J. Barkovich, Concepts of myelin and myelination in neuroradiology, Am. J. Neuroradiol. 21 (2000) 1099–1109.

[10] Battin M., Rutherford M.A., Magnetic resonance imaging of the brain in preterm infants: 24 weeks’ gestation to term, in: M.A. Rutherford (Ed.), MRI of the Neonatal Brain.

[11] B. Belaroussi, J. Milles, S. Carme, Y.M. Zhu, H.B Cattin, Intensity nonuniformity correction in MRI: existing methods and their validation, Med. Image Anal. 10 (2006) 234–246.

[12] S. Beucher, F. Meyer, The morphological approach to segmentation: the watershed transformation, in: E.R. Dougherty (Ed.), Mathematical Morphology in Image Processing, CRC Press, 1993, pp. 433–481.

[13] S.T. Brady, A.S. Witt, L.L. Kirkpatrick, S.M. de Waegh, C. Readhead, et al., Formation of compact myelin is required for maturation of the axonal cytoskeleton, J. Neurosci. 19 (1999) 7278–7288.

[14] Jaware, T. H., Khanchandani, K. B., & Kalal, D. (2019). An atlas-free newborn brain image segmentation and classification scheme based on Som-DCNN with sparse auto encoder. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 8(1), 49– 64.

[15] Bui, T. D., Wang, L., Lin, W., Li, G., & Shen, D. 6-month infant brain MRI segmentation guided by 24-month data using cycle-consistent adversarial networks. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[16] Zeng, Z., Zhao, T., Sun, L., Zhang, Y., Xia, M., Liao, X., Zhang, J., Shen, D., Wang, L., & He, Y. (2021, January 1).3D-masnet: 3D mixed-scale asymmetric convolutional segmentation network for 6-month-old Infant Brain Mr Images. bioRxiv. March 7, 2022.

[17] Yue Sun, Kun Gao, Sijie Niu, Weili Lin, Gang Li, Li Wang, The UNC/UMN Baby Connectome Project Consortium,. (2020, October 4). Semi-supervised transfer learning for infant cerebellum tissue segmentation. SpringerLink. March 7, 2022.

[18] Tushar Jaware, Kamlesh Khanchandani & Ravindra Badgujar (2020) A novel hybrid atlas-free hierarchical graphbased segmentation of newborn brain MRI using wavelet filter banks, International Journal of Neuroscience, 130:5, 499-514.

[19] B. Surányi, L. Kovacs and L. Szilagyi, "Segmentation of Brain Tissues from Infant MRI Records Using Machine Learning Techniques," 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), 2021, pp. 000455-000460.

[20] Sadegh Pasban,Sajad Mohamadzadeh,Javad ZeraatkarMoghaddam,Amir Keivan Shafiei,Infant brain segmentation based on a combination of VGG-16 and U-Net deep neural networks ,IET image process. 2020;14(17):4756-65, DOI: 10.1049/iet-ipr.2020.0469.

[21] Zhou, H., Zhang, Y., Huang, D., Li, L. (2013). Semi-supervised Learning with Transfer Learning. In: Sun, M., Zhang, M., Lin, D., Wang, H. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2013 2013. Lecture Notes in Computer Science (), vol 8202. Springer, Berlin, Heidelberg. 11.

[22] Tushar H. Jaware and K. B. Khanchandani and Durgeshwari Kalal,An atlas-free newborn brain image segmentation and classification scheme based on SOM-DCNN with sparse auto encoder,Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol 8, pages 49-64, 2020, Taylor & Francis.

[23] Patil Vinodkumar Ramesh1 , Jaware Tushar Hrishikesh2 , Manisha S. Patil3, “Infant’s MRI Brain Tissue Segmentation using Integrated CNN Feature Extractor and Random Forest”, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 11 Issue: 1s DOI: https://doi.org/10.17762/ijritcc.v11i1s.6002 Article Received: 27 November 2022 Revised: 20 December 2022 Accepted: 08 January 2023

[24] Jad Dino Raad1 , Ratna Babu Chinnam1 , SuzanArslanturk 2*, SidharthaTan3 , Jeong Won Jeong3 & Swati Mody4, “Unsupervised abnormality detection in neonatal MRI brain scans using deep learning”,

[25] Sumitra V, Latha P, Sasikala, Hedayuth Basha, Lalitha Sudha,R.Gnanavel. (2022). Infant Brain Mri Abnormalities Detection Using Deep Learning. Computer Integrated Manufacturing Systems, 28(12), 663–678. Retrieved from http://cims-journal.com/index.php/CN/article/view/450

[26] Khalili, N., Lessmann, N., Turk, E., Claessens, N., de Heus, R., Kolk, T., Viergever, M.A., Benders, M.J., and Išgum, I. Automatic Brain Tissue Segmentation in Fetal MRI using Convolutional Neural Networks. Magnetic resonance imaging, vol. 64, pp. 77-89, 2019.

[27] Kornilov, A., Safonov, I., and Yakimchuk, I. A Review of Watershed Implementations for Segmentation of Volumetric Images. Journal of Imaging, vol. 8, no. 5, pp. 127, 2022

[28] N. Suresh Kumar* and Amit Kumar Goel, “Detection, Localization and Classification of Fetal Brain Abnormalities using YOLO v4 Architecture”,International Journal of Performability Engineering, vol. 18, no. 10, October 2022, pp. 720-729.

[29] A.A. Movassagh, J.A. Alzubi, M. Gheisari, M. Rahimi, S. Mohan, A.A. Abbasi, N. Nabipour, Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model, J. Ambient Intell. Hum. Comput. 14 (2021) 6017–6025.

[30] Jafar A. Alzubi, Balasubramaniyan Bharathikannan, Sudeep Tanwar, Ramachandran Manikandand, Ashish Khanna, Chandrasekar Thaventhiran, Boosted neural network ensemble classification for lung cancer disease diagnosis, Appl. Soft Comput. 80 (July 2019) 579–591.

[31] O. Attallah, H. Gadelkarim, M.A. Sharkas, Detecting and classifying fetal brain abnormalities using machine learning techniques, in: 2018 17th IEEE International Conference on Machine Learning and Applications, ICMLA, 2018, pp. 1371–1376.

[32] B.J. Erickson, P. Korfiatis, Z. Akkus, T.L. Kline, Machine learning for medical imaging, Radiographics 37 (2) (2017) 505.

[33] G. Gandolfi Colleoni, E. Contro, A. Carletti, T. Ghi, G. Campobasso, G. Rembouskos, G. Volpe, G. Pilu, P. Volpe, Prenatal diagnosis and outcome of fetal posterior fossa fluid collections, Ultrasound Obstet. Gynecol. 39 (6) (2012) 625–631.

[34] E. Katorza, I. Gat, N. Duvdevani, N. Meller, N. Pardo, E. Barzilay, R. Achiron, Fetal brain anomalies detection during the first trimester: expanding the scope of antenatal sonography, J. Matern. Fetal Neonatal Med. 31 (4) (2018) 506–512.

[35] N. Khalili, N. Lessmann, E. Turk, N. Claessens, R. de Heus, T. Kolk, M.A. Viergever, M.J.N.L. Benders, I. Iˇsgum, Automatic brain tissue segmentation in fetal MRI using convolutional neural networks, Magn. Reson. Imag. 64 (2019) 77–89, https://doi. org/10.1016/j.mri.2019.05.020.

[36] D.J. Lowsky, Y. Ding, D.K.K. Lee, C.E. McCulloch, L.F. Ross, J.R. Thistlethwaite, S. A. Zenios, AK-nearest neighbors survival probability prediction method, Stat. Med. 32 (12) (2013) 2062–2069.

[37] M. Sanz-Cort´es, F. Figueras, E. Bonet-Carne, N. Padilla, V. Tenorio, N. Bargallo, I. Amat-Roldan, E. Gratacos, ´ Fetal brain MRI texture analysis identifies different microstructural patterns inadequate and small for gestational age fetuses at term, Fetal Diagn. Ther. 33 (2) (2013) 122–129.

[38] M. Sanz-Cortes, G.A. Ratta, F. Figueras, E. Bonet-Carne, N. Padilla, A. Arranz, N. Bargallo, E. Gratacos, Automatic quantitative MRI texture analysis in small-forgestational-age fetuses discriminates abnormal neonatal neurobehavior, PLoS One 8 (7) (2013), e69595.

[39] M. Priya, M. Nandhini, “Detection of fetal brain abnormalities using data augmentation and convolutional neural network in internet of things”, Available online 14 June 2023 2665-9174/© 2023 Published by Elsevier Ltd.

[40] V. Anitha S. Murugavalli, “Brain Tumour Classification Using Two-Tier Classifier With Adaptive Segmentation Technique. 2016

[41] G Ball, “Machine-Learning To Characterise Neonatal Functional Connectivity In The Preterm Brain”, Neuroimage 2016

[42] Bradley J.Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy L. Klinefeb, “Machine Learning For Medical Imaging”, 2017

[43] Christopher D. Smyser, Nico U.F. Dosenbach, [...], And Jeffrey J. Neil “Prediction Of Brain Maturity In Infants Using Machine-Learning Algorithms”, 2016

[44] Derek Bradley, Gerhard Roth , “Adaptive Thresholding Using The Integral Image”, 30 Jan 2011.

[45] Eldad Katorza Et Al. J Matern Fetal Neonatal Med , “Fetal Brain Anomalies Detection During The First Trimester: Expanding The Scope Of Antenatal Sonography”, 2018.

[46] Magdalena Sanz-Cortes Et Al. Plos One , “Automatic Quantitative Mri Texture Analysis In Small-For-Gestational-Age Fetuses Discriminates Abnormal Neonatal Neurobehavior “, 2013

[47] Mohammad Havaeia,1, Axel Davyb, David Warde-Farleyc, Antoine Biardc,D, Aaron Courvillec, Yoshua Bengioc, Chris Palc,E,Pierre-Marc Jodoina, Hugo Larochellea,F “Brain Tumor Segmentation With Deep Neural Networks” [Received 27 April 2015, Revised 2 March 2016, Accepted 11 May 2016, Available Online 19 May 2016.]

[48] Yan Jin Et Al. Hum , “Identification Of Infants At High-Risk For Autism Spectrum Disorder Using Multiparameter Multiscale White Matter Connectivity Networks”, [Brain Mapp2015 Dec].

Abstract

References

How to cite