A Comparative Assessment of Different Approaches of Segmentation and Classification Methods on Childhood Medulloblastoma Images

Computational pathology involves the analysis of pathological images at two powers of microscopic examination: low (or architectural) power and high (or cell) power. Analysis at both these levels is highly crucial for treatment planning, or prognosis, of the patient. The present paper is a study on childhood medulloblastoma (CMB) using an indigenously collected image dataset. The region of interest (RoI) for the low power is patches (or sections) from the architectural level and for the high power, the nucleus. Four deep learning semantic segmentation and eight machine learning segmentation algorithms were compared and evaluated on the same dataset. The performance was measured using the Jaccard coefficient, which established the superiority of Fractal Net with 79.21% over other algorithms. Metrics such as Accuracy, Dice coefficient, F1-Score, Loss, Precision and Recall were used to compare the deep learning segmentation methods. Jaccard loss was used as an evaluation matrix for the traditional segmentation experiments. Subsequently, classification experiments were performed for comparison at both the powers and binary (normal vs abnormal) as well as multilevel (four subtypes of CMB) classification. The cell-based classification study showed 95.4% and 62.1% accuracy for binary and multi-level, respectively. Here, the features texture, shape, and color contributed to optimum classification. Next, the patch-based classification experiments involved a comparison of texture analysis using machine learning methods with two pre-trained deep learning classification models: Alexnet and VGG-16, using a softmax classifier. Here, it was observed that machine learning models outperform the deep learning models with 100% and 91.3% accuracy for both binary and multi-level, respectively. We hypothesize that combining both architectural and cell classification could lead to a more effective prognosis. The strength of the paper is the combined segmentation and classification study at two powers of microscope magnification using both classical machine learning as well as current deep learning techniques.

[1]  Anant Madabhushi,et al.  Segmentation of nodular medulloblastoma using Random Walker and Hierarchical Normalized Cuts , 2011, 2011 IEEE 37th Annual Northeast Bioengineering Conference (NEBEC).

[2]  Amir H. Gandomi,et al.  The Arithmetic Optimization Algorithm , 2021, Computer Methods in Applied Mechanics and Engineering.

[3]  Srikanth Tammina,et al.  Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images , 2019, International Journal of Scientific and Research Publications (IJSRP).

[4]  Bahram Parvin,et al.  Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association , 2013, IEEE Transactions on Medical Imaging.

[5]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Johan Isaksson,et al.  Semantic segmentation of microscopic images of H&E stained prostatic tissue using CNN , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[7]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[8]  Constantino Carlos Reyes-Aldasoro,et al.  Texture Segmentation: An Objective Comparison between Five Traditional Algorithms and a Deep-Learning U-Net Architecture , 2019, Applied Sciences.

[9]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[10]  Amjad Rehman,et al.  Computer-assisted brain tumor type discrimination using magnetic resonance imaging features , 2018, Biomedical engineering letters.

[11]  Prabin Kumar Bora,et al.  Pattern Recognition and Machine Intelligence: 8th International Conference, PReMI 2019, Tezpur, India, December 17-20, 2019, Proceedings, Part II , 2019, PReMI.

[12]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Emre Dandil,et al.  Computer-Aided Diagnosis of Malign and Benign Brain Tumors on MR Images , 2014, ICT Innovations.

[14]  Arvid Lundervold,et al.  An overview of deep learning in medical imaging focusing on MRI , 2018, Zeitschrift fur medizinische Physik.

[15]  Richard A. Moffitt,et al.  Computer Aided Histopathological Classification of Cancer Subtypes , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

[16]  Gregory Shakhnarovich,et al.  FractalNet: Ultra-Deep Neural Networks without Residuals , 2016, ICLR.

[17]  Omneya Attallah,et al.  MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI , 2021, Diagnostics.

[18]  Angel Cruz-Roa,et al.  A method for medulloblastoma tumor differentiation based on convolutional neural networks and transfer learning , 2015, Symposium on Medical Information Processing and Analysis.

[19]  Francesco Bianconi,et al.  Multi-class texture analysis in colorectal cancer histology , 2016, Scientific Reports.

[20]  Chandan Chakraborty,et al.  Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation , 2018, IEEE Transactions on Image Processing.

[21]  Juan Villegas-Cortez,et al.  Unsupervised Font Clustering Using Stochastic Versio of the EM Algorithm and Global Texture Analysis , 2004, CIARP.

[22]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[23]  Lipi B. Mahanta,et al.  Study on Contribution of Biological Interpretable and Computer-Aided Features Towards the Classification of Childhood Medulloblastoma Cells , 2018, J. Medical Syst..

[24]  Li Sun,et al.  Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning , 2019, Front. Neurosci..

[25]  Andrew H. Beck,et al.  Abstract LB-285: Computational pathology for predicting prostate cancer recurrence , 2015 .

[26]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[27]  Kathleen M Schmainda,et al.  Computer‐aided detection of brain tumor invasion using multiparametric MRI , 2009, Journal of magnetic resonance imaging : JMRI.

[28]  P. G. Tahoces,et al.  Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms. , 1998, Medical physics.

[29]  Anant Madabhushi,et al.  An integrated texton and bag of words classifier for identifying anaplastic medulloblastomas , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  Lipi B. Mahanta,et al.  On the Study of Childhood Medulloblastoma Auto Cell Segmentation from Histopathological Tissue Samples , 2019, PReMI.

[31]  Tom Drummond,et al.  A review of deep learning in the study of materials degradation , 2018, npj Materials Degradation.

[32]  Emanuele Frontoni,et al.  Convolutional Networks for Semantic Heads Segmentation using Top-View Depth Data in Crowded Environment , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[33]  S Saimar Khan,et al.  Robust cell detection of histopathological brain tumor images and analyzing its textual features , 2017, 2017 2nd International Conference on Communication and Electronics Systems (ICCES).

[34]  Adel Hafiane,et al.  Integrating segmentation with deep learning for enhanced classification of epithelial and stromal tissues in H&E images , 2017, Pattern Recognit. Lett..

[35]  Alexis B. Carter,et al.  Computational Pathology: A Path Ahead. , 2016, Archives of pathology & laboratory medicine.

[36]  Roman Monczak,et al.  Computer-Aided Breast Cancer Diagnosis Based on the Analysis of Cytological Images of Fine Needle Biopsies , 2013, IEEE Transactions on Medical Imaging.

[37]  Anant Madabhushi,et al.  A texture-based classifier to discriminate anaplastic from non-anaplastic medulloblastoma , 2011, 2011 IEEE 37th Annual Northeast Bioengineering Conference (NEBEC).

[38]  Mrinal Mandal,et al.  A robust automatic nuclei segmentation technique for quantitative histopathological image analysis. , 2012, Analytical and quantitative cytopathology and histopathology.

[39]  Thomas Brox,et al.  U-Net: deep learning for cell counting, detection, and morphometry , 2018, Nature Methods.

[40]  P. Burger,et al.  Histopathologic grading of medulloblastomas , 2002, Cancer.

[41]  Kenneth Revett,et al.  Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm , 2014, Expert Syst. Appl..

[42]  Guang Yang,et al.  Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks , 2017, MIUA.

[43]  R. Kumar,et al.  Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features , 2015, Journal of medical engineering.

[44]  Laith Abualigah,et al.  Advances in Sine Cosine Algorithm: A comprehensive survey , 2021, Artif. Intell. Rev..

[45]  Lipi B Mahanta,et al.  Classification of childhood medulloblastoma into WHO‐defined multiple subtypes based on textural analysis , 2020, Journal of microscopy.