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Automated Conversion of Ultrasound Pixel Dimensions to Millimeters using Deep Learning Models

Published: 20 August 2023 Publication History

Abstract

Ultrasound imaging is a widely used method in prenatal care to obtain fetal biometrics. The automatic conversion of these biometrics from pixels to millimeters (mm) by the ultrasound machine enables physicians to evaluate fetal development. However, the metadata file containing pixel dimensions is often incomplete or missing, presenting a challenge for developing artificial intelligence (AI) applications for fetal ultrasound images. This study proposes a solution that employs pre-trained deep learning models to predict pixel size in mm, thereby automating the labeling process for building AI applications for fetal ultrasound images. The study utilized 2,835 fetal head ultrasound images to train, validate, and test six deep-learning regression models for the conversion of pixels to mm. The evaluation of the deep-learning models involved three steps: traditional evaluation metrics, descriptive analysis, and statistical approach. The results from the three evaluation stages showed that the Xception model outperformed the other models, achieving an R-squared (R2) value of 0.8535 and a mean squared error (MSE) of 0.00028 when predicting pixel size in mm on the test dataset. The descriptive analysis yielded a standard deviation (SD) of 0.0449, while Spearman’s rank correlation coefficient was 0.841.

References

[1]
Mahmood Alzubaidi. 2022. Converting Pixel into millimeter in ultrasound images: Technique and dataset. https://doi.org/10.5281/zenodo.7193337
[2]
Mahmood Alzubaidi, Marco Agus, Khalid Alyafei, Khaled A. Althelaya, Uzair Shah, Alaa Abd-Alrazaq, Mohammed Anbar, Michel Makhlouf, and Mowafa Househ. 2022. Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images. iScience 25, 8 (2022), 104713. https://doi.org/10.1016/j.isci.2022.104713
[3]
Mahmood Alzubaidi, Marco Agus, Uzair Shah, Michel Makhlouf, Khalid Alyafei, and Mowafa Househ. 2022. Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction. Diagnostics 12, 9 (2022). https://doi.org/10.3390/diagnostics12092229
[4]
Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux. 2013. API design for machine learning software: experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning. 108–122.
[5]
S. Campbell and Alison Thoms. 1977. ULTRASOUND MEASUREMENT OF THE FETAL HEAD TO ABDOMEN CIRCUMFERENCE RATIO IN THE ASSESSMENT OF GROWTH RETARDATION. BJOG: An International Journal of Obstetrics & Gynaecology 84, 3 (1977), 165–174. https://doi.org/10.1111/j.1471-0528.1977.tb12550.x
[6]
Francois Chollet 2015. Keras. https://github.com/fchollet/keras
[7]
S Degani. 2001. Fetal biometry: clinical, pathological, and technical considerations. Obstet Gynecol Surv 56, 3 (March 2001), 159–167.
[8]
Jan Hauke and Tomasz Kossowski. 2011. Comparison of Values of Pearson’s and Spearman’s Correlation Coefficients on the Same Sets of Data. Quaestiones Geographicae 30, 2 (2011), 87–93. https://doi.org/
[9]
T Joel and R Sivakumar. 2013. Despeckling of ultrasound medical images: A survey. Journal of Image and Graphics 1, 3 (2013), 161–165.
[10]
Megan Maar, Juhyun Lee, Anthony Tardi, Yuan-Yi Zheng, Candance Wong, and Jing Gao. 2022. Inter-transducer variability of ultrasound image quality in obese adults: Qualitative and quantitative comparisons. Clinical Imaging 92 (2022), 63–71. https://doi.org/10.1016/j.clinimag.2022.09.010
[11]
Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, and Anant Sahai. 2021. Classification vs Regression in Overparameterized Regimes: Does the Loss Function Matter?J. Mach. Learn. Res. 22, 1, Article 222 (jan 2021), 69 pages.
[12]
William R. Riddle and David R. Pickens. 2005. Extracting data from a DICOM file. Medical Physics 32, 6Part1 (2005), 1537–1541. https://doi.org/10.1118/1.1916183 arXiv:https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1118/1.1916183
[13]
Elisabetta Sassaroli, Calum Crake, Andrea Scorza, Don-Soo Kim, and Mi-Ae Park. 2019. Image quality evaluation of ultrasound imaging systems: advanced B-modes. Journal of Applied Clinical Medical Physics 20, 3 (2019), 115–124. https://doi.org/10.1002/acm2.12544
[14]
Thomas L. A. van den Heuvel, Dagmar de Bruijn, Chris L. de Korte, and Bram van Ginneken. 2018. Automated measurement of fetal head circumference using 2D ultrasound images. PLOS ONE 13, 8 (08 2018), 1–20. https://doi.org/10.1371/journal.pone.0200412
[15]
Qi Wang, Yue Ma, Kun Zhao, and Yingjie Tian. 2022. A Comprehensive Survey of Loss Functions in Machine Learning. Annals of Data Science 9, 2 (01 Apr 2022), 187–212. https://doi.org/10.1007/s40745-020-00253-5
[16]
Shoya Yamagishi, Keisuke Doman, Yoshito Mekada, Naoshi Nishida, and Masatoshi Kudo. 2022. Detection and tracking of liver tumors for ultrasound diagnostic support using deep learning. Journal of Image and Graphics 10, 1 (2022), 50–55.
[17]
Füsun Yasar, Burcu Apaydin, and Hasan-Hüseyin Yilmaz. 2012. The effects of image compression on quantitative measurements of digital panoramic radiographs. Med Oral Patol Oral Cir Bucal 17, 6 (Nov. 2012), e1074–81.

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      ICCTA '23: Proceedings of the 2023 9th International Conference on Computer Technology Applications
      May 2023
      270 pages
      ISBN:9781450399579
      DOI:10.1145/3605423
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 20 August 2023

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      Author Tags

      1. Fetal head
      2. Ultrasound images
      3. deep learning
      4. pixel to millimeter

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