Machine-learning-based classification of co-cultured cells from the phase images in digital holographic microscopy

Harshal Chaudhari; Rishikesh Kulkarni; M. K. Bhuyan; Pradeep K. Sundaravadivelu; Rajkumar Thummer

doi:10.1117/12.3017346

18 June 2024 Machine-learning-based classification of co-cultured cells from the phase images in digital holographic microscopy

Harshal Chaudhari, Rishikesh Kulkarni, M. K. Bhuyan, Pradeep K. Sundaravadivelu, Rajkumar Thummer

Author Affiliations +

Proceedings Volume 12996, Unconventional Optical Imaging IV; 129960H (2024) https://doi.org/10.1117/12.3017346
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

In the tumor microenvironment, cell interactions play a crucial role in influencing the morphology and metastasis characteristics of non-cancerous and cancerous cells. Although machine learning techniques have been proven effective in classifying individually cultured cell lines, their accuracy may deteriorate in classifying a co-cultured mixture of cells. In the proposed work, the optical path difference images of human dermal fibroblast and melanoma A375 cells were recorded, individually and in a mixture, using the off-axis digital holographic microscopy setup. A dataset consisted of segmented and labeled images of both sample types. The dataset was used to train a convolutional neural network through transfer learning to extract the morphology relevant features. An XGBoost classifier trained on the extracted features is found to effectively recognize morphological changes in cells within a mixture and classify them accurately with a limited size dataset.

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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Harshal Chaudhari, Rishikesh Kulkarni, M. K. Bhuyan, Pradeep K. Sundaravadivelu, and Rajkumar Thummer "Machine-learning-based classification of co-cultured cells from the phase images in digital holographic microscopy", Proc. SPIE 12996, Unconventional Optical Imaging IV, 129960H (18 June 2024); https://doi.org/10.1117/12.3017346

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