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

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Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusion

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.

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Data Availability

The images will be made public after we complete our work.

Code Availability

The code can be made available on request.

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Acknowledgements

We are thankful to the Director, Institute of Advanced Study in Science and Technology, Guwahati for providing us with the platform to perform our research. We convey our sincere thanks to Shabnam Ahmed, GNRC, Dispur for her time and support during the clinical assessment. We are also thankful to Dr Basanta K. Baishya and Dr Inamul Haque, GNRC for providing us with the medical data and Dr Anup Kumar Das, Ayursundra Pvt. Ltd for performing the staining of our slides.

Funding

The research is supported by the Institute of Advanced Study in Science and Technology, An Autonomous Institute under DST, govt. of India.

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Authors and Affiliations

  1. Mathematical and Computational Sciences Division, Institute of Advanced Study in Science and Technology, Guwahati, 781035, India

    Daisy Das & Lipi B. Mahanta

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  1. Daisy Das
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  2. Lipi B. Mahanta
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Contributions

DD performed the experiments and analysis part and also the writing of the paper, LBM was behind the conceptualization of the idea and equally contributed towards the drafting of the paper.

Corresponding author

Correspondence to Lipi B. Mahanta.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

Permission for the study was given by the ethical bodies of both the participating institutions under IASST: Registration number ECR/248/Indt/AS/2015 of Rule 122DD, Drugs and Cosmetics Rule, 1945 of India and GMCH: MC/190/2007/pt-1/E-C/32 dated 30.5.2017.

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Yes, all authors.

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Das, D., Mahanta, L.B. A Comparative Assessment of Different Approaches of Segmentation and Classification Methods on Childhood Medulloblastoma Images. J. Med. Biol. Eng. 41, 379–392 (2021). https://doi.org/10.1007/s40846-021-00612-4

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