365
Views
3
CrossRef citations to date
0
Altmetric
Research Article

Deep learning and radiomics based automatic diagnosis of hippocampal sclerosis

, , , , , , , , , , & show all
Pages 947-958 | Received 08 Apr 2021, Accepted 08 Dec 2021, Published online: 28 Dec 2021

References

  • Tellez-Zenteno JF, Hernandez Ronquillo L, Moien-Afshari F, et al. Surgical outcomes in lesional and non-lesional epilepsy: a systematic review and Meta-analysis. Epilepsy Res. 2010;89(2–3):310–318.
  • Harroud A, Bouthillier A, Weil AG, et al. Temporal lobe epilepsy surgery failures: a review. Epilepsy Res Treat. 2012;2012:201651.
  • Mo J, Liu Z, Sun K, et al. Automated detection of hippocampal sclerosis using clinically empirical and radiomics features. Epilepsia. 2019;60(12):2519–2529.
  • Feng R, Farrukh Hameed NU, Hu J, et al. Ictal stereo-electroencephalography onset patterns of mesial temporal lobe epilepsy and their clinical implications. Clin Neurophysiol. 2020;131(9):2079–2085.
  • Fan Z, Sun B, Lang L-Q, et al. Diagnosis and surgical treatment of non-lesional temporal lobe epilepsy with unilateral amygdala enlargement. Neurol Sci. 2021;42(6):2353–2361.
  • Caciagli L, Bernasconi A, Wiebe S, et al. A meta-analysis on progressive atrophy in intractable temporal lobe epilepsy. Neurology. 2017;89(5):506–516.
  • Bernasconi A, Cendes F, Theodore WH, et al. Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: a consensus report from the international league against epilepsy neuroimaging task force. Epilepsia. 2019;60(6):1054–1068.
  • Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology. 2016;278(2):563–577.
  • Acharya UR, Hagiwara Y, Sudarshan VK, et al. Towards precision medicine: from quantitative imaging to radiomics. J Zhejiang Univ Sci B. 2018;19(1):6–24.
  • Prasanna P, Tiwari P, Madabhushi A. Co-occurrence of local anisotropic gradient orientations (CoLlAGe): a new radiomics descriptor. Sci Rep. 2016;6(1):37241.
  • Parmar C, Leijenaar RTH, Grossmann P, et al. Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci Rep. 2015;5(1):11044.
  • Teruel JR, Heldahl MG, Goa PE, et al. Dynamic contrast-enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. NMR Biomed. 2014;27(8):887–896.
  • Silva G, Martins C, Moreira da Silva N, et al. Automated volumetry of hippocampus is useful to confirm unilateral mesial temporal sclerosis in patients with radiologically positive findings. Neuroradiol J. 2017;30(4):318–323.
  • Park YW, Choi YS, Kim SE, et al. Radiomics features of hippocampal regions in magnetic resonance imaging can differentiate medial temporal lobe epilepsy patients from healthy controls. Sci Rep. 2020;10(1):19567.
  • Princich JP, Donnelly-Kehoe PA, Deleglise A, et al. Diagnostic performance of MRI volumetry in epilepsy patients with hippocampal sclerosis supported through a random Forest automatic classification Algorithm. Front Neurol. 2021;12:613967.
  • Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15(1):273–289.
  • Mazziotta J, Toga A, Evans A, et al. A probabilistic atlas and reference system for the human brain: International consortium for brain mapping (ICBM). Philos Trans R Soc Lond Ser B Biol Sci. 2001;356(1412):1293–1322.
  • Rolls ET, Huang CC, Lin CP, et al. Automated anatomical labelling atlas 3. Neuroimage. 2020;206:116189.
  • Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells, WM and Frangi AF, editors. Medical image computing and Computer-Assisted intervention – MICCAI 2015. Cham: Springer International Publishing; 2015. p. 234–241.
  • Oktay O, Schlemper J, Folgoc LL, et al. Attention u-net: learning where to look for the pancreas. 2018.
  • Vallières M, Freeman CR, Skamene SR, et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol. 2015;60(14):5471–5496.
  • Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging. 2004;22(1):81–91. doi:.
  • Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst, Man, Cybern. 1973;SMC-3(6):610–621.
  • Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Computat. 2002;6(2):182–197.
  • Yu J, Shi Z, Lian Y, et al. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol. 2017;27(8):3509–3522. [Online]. Available: http://inis.iaea.org/search/search.aspx?orig_q=RN:48084279.
  • Wu G, Chen Y, Wang Y, et al. Sparse Representation-Based radiomics for the diagnosis of brain tumors. IEEE Trans Med Imaging. 2018;37(4):893–905.
  • Fischl B, Salat DH, Busa E, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33(3):341–355.
  • Manjón JV, Coupé PJFIN. volBrain: an online MRI brain volumetry system. Front Neuroinform. 2016;10:30.
  • Brinkmann BH, Guragain H, Kenney-Jung D, et al. Segmentation errors and intertest reliability in automated and manually traced hippocampal volumes. Ann Clin Transl Neurol. 2019;6(9):1807–1814.
  • Mettenburg J, Branstetter B, Wiley C, et al. Improved detection of subtle mesial temporal sclerosis: validation of a commercially available software for automated segmentation of hippocampal volume. Am J Neuroradiol. 2019;40(3):440–445.
  • Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2011;2(3):1–27.
  • Burges CJJDM, Discovery K. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov. 1998;2(2):121–167.
  • Feng R, Hu J, Wu J, et al. Comprehensive preoperative work-up and surgical treatment of low grade tumor/benign lesion related temporal lobe epilepsy. Journal of Clinical Neuroscience. 2017;39:203–208.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.