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The Impact of Data Preprocessing on the Accuracy of CNN-Based Heart Segmentation

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Progress in Image Processing, Pattern Recognition and Communication Systems (CORES 2021, IP&C 2021, ACS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 255))

Abstract

Whole heart segmentation significantly improves the diagnostic value of computed tomography images. Since manual segmentation is very time consuming, efforts have been made to automate this process, especially using deep learning methods such as convolutional neural networks (CNN). This paper considers how preprocessing of computed tomography with the Statistical Dominance Algorithm would affect the results of segmentation obtained with CNNs. Segmentation results were compared to original CTs and processed images. Compared to unprocessed data, improvements in segmentation accuracy were obtained after image processing. Using three-fold cross-validation, the average Dice similarity coefficient that was achieved was 0.811 for unprocessed images and 0.863 for processed images.

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Acknowledgement

This work was supported by the European Union under the Regional Operational Programme for Malopolska Region 2014–2020, project no: RPMP.01.02.01-12-0027/19.

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Authors and Affiliations

  1. Medical Simulation Technologies sp. z o.o, Miechowska 5b m 4, 30–055, Krakow, Poland

    Julia Lasek

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  1. Julia Lasek
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Correspondence to Julia Lasek .

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Editors and Affiliations

  1. Institute of Computer Science and Telecommunications, University of Science and Technlogy, Bydgoszcz, Poland

    Michal ChoraÅ›

  2. Institute of Computer Science and Telecommunications, University of Science and Technology, Bydgoszcz, Poland

    Ryszard S. ChoraÅ›

  3. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Marek Kurzyński

  4. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Paweł Trajdos

  5. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Jerzy PejaÅ›

  6. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Tomasz Hyla

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Lasek, J. (2022). The Impact of Data Preprocessing on the Accuracy of CNN-Based Heart Segmentation. In: Choraś, M., Choraś, R.S., Kurzyński, M., Trajdos, P., Pejaś, J., Hyla, T. (eds) Progress in Image Processing, Pattern Recognition and Communication Systems. CORES IP&C ACS 2021 2021 2021. Lecture Notes in Networks and Systems, vol 255. Springer, Cham. https://doi.org/10.1007/978-3-030-81523-3_17

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