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A Deep Learning Based Approach to Semantic Segmentation of Lung Tumour Areas in Gross Pathology Images

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Medical Image Understanding and Analysis (MIUA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14122))

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Abstract

Gross pathology photography of surgically resected specimens is an often overlooked modality for the study of medical images that can provide and document useful information about a tumour before it is distorted by slicing. A method for the automatic segmentation of tumour areas in this modality could provide a useful tool for both pathologists and researchers. We propose the first deep learning based methodology for the automatic segmentation of tumour areas in gross pathological images of lung cancer specimens. The semantic segmentation models applied are Deeplabv3+ with both a MobileNet and Resnet50 backbone as well as UNet, all models were trained and tested with both a DICE and cross entropy loss function. Also included is a pre and post-processing pipeline for the input images and output segmentations respectively. The final model is formed of an ensemble of all the trained networks which produced a tumour pixel-wise accuracy of 69.7% (96.8% global accuracy) and tumour area IoU score of 0.616. This work on this novel application highlights the challenges with implementing a semantic segmentation model in this domain that have not been previously documented.

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Acknowledgements

We would like to thank the Beatson Cancer Charity and UKRI EPSRC for funding this work as well as the CDT in Applied Photonics for facilitating this work.

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Authors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Matthew Gil, Paul Murray, Gabriel Reines March & Stephen Marshall

  2. NHS Greater Glasgow and Clyde, Glasgow, UK

    Matthew Gil, Craig Dick & Gabriel Reines March

  3. NHS Lothian, Edinburgh, UK

    Stephen Harrow

Authors
  1. Matthew Gil
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  2. Craig Dick
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  3. Stephen Harrow
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  4. Paul Murray
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  5. Gabriel Reines March
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  6. Stephen Marshall
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Corresponding author

Correspondence to Matthew Gil .

Editor information

Editors and Affiliations

  1. University of Aberdeen, Aberdeen, UK

    Gordon Waiter

  2. University of Aberdeen, Aberdeen, UK

    Tryphon Lambrou

  3. University of Aberdeen, Aberdeen, UK

    Georgios Leontidis

  4. University of Aberdeen, Aberdeen, UK

    Nir Oren

  5. University of Aberdeen, Aberdeen, UK

    Teresa Morris

  6. University of Aberdeen, Aberdeen, UK

    Sharon Gordon

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Gil, M., Dick, C., Harrow, S., Murray, P., March, G.R., Marshall, S. (2024). A Deep Learning Based Approach to Semantic Segmentation of Lung Tumour Areas in Gross Pathology Images. In: Waiter, G., Lambrou, T., Leontidis, G., Oren, N., Morris, T., Gordon, S. (eds) Medical Image Understanding and Analysis. MIUA 2023. Lecture Notes in Computer Science, vol 14122. Springer, Cham. https://doi.org/10.1007/978-3-031-48593-0_2

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  • DOI: https://doi.org/10.1007/978-3-031-48593-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48592-3

  • Online ISBN: 978-3-031-48593-0

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