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Fetal Ultrasound Segmentation and Measurements Using Appearance and Shape Prior Based Density Regression with Deep CNN and Robust Ellipse Fitting

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Abstract

Accurately segmenting the structure of the fetal head (FH) and performing biometry measurements, including head circumference (HC) estimation, stands as a vital requirement for addressing abnormal fetal growth during pregnancy under the expertise of experienced radiologists using ultrasound (US) images. However, accurate segmentation and measurement is a challenging task due to image artifact, incomplete ellipse fitting, and fluctuations due to FH dimensions over different trimesters. Also, it is highly time-consuming due to the absence of specialized features, which leads to low segmentation accuracy. To address these challenging tasks, we propose an automatic density regression approach to incorporate appearance and shape priors into the deep learning-based network model (DR-ASPnet) with robust ellipse fitting using fetal US images. Initially, we employed multiple pre-processing steps to remove unwanted distortions, variable fluctuations, and a clear view of significant features from the US images. Then some form of augmentation operation is applied to increase the diversity of the dataset. Next, we proposed the hierarchical density regression deep convolutional neural network (HDR-DCNN) model, which involves three network models to determine the complex location of FH for accurate segmentation during the training and testing processes. Then, we used post-processing operations using contrast enhancement filtering with a morphological operation model to smooth the region and remove unnecessary artifacts from the segmentation results. After post-processing, we applied the smoothed segmented result to the robust ellipse fitting-based least square (REFLS) method for HC estimation. Experimental results of the DR-ASPnet model obtain 98.86% dice similarity coefficient (DSC) as segmentation accuracy, and it also obtains 1.67 mm absolute distance (AD) as measurement accuracy compared to other state-of-the-art methods. Finally, we achieved a 0.99 correlation coefficient (CC) in estimating the measured and predicted HC values on the HC18 dataset.

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Data Availability

The dataset is public and can be downloaded from https://hc18.grand-challenge.org/.

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Authors and Affiliations

  1. Department of Computer Science, KIET Group of Institutions, Delhi-NCR, Ghaziabad, U.P, India

    Gaurav Dubey

  2. ABES Engineering College, Ghaziabad, U.P, India

    Somya Srivastava

  3. Amity University, Noida, Uttar Pradesh, India

    Anant Kumar Jayswal

  4. Department of Computer Science, Bennett University, Greater Noida, India

    Mala Saraswat

  5. Shiv Nadar University, Greater Noida, Uttar Pradesh, India

    Pooja Singh

  6. CSE Department, UIT, Uttaranchal University, Dehradun, Uttarakhand, India

    Minakshi Memoria

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  4. Mala Saraswat
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Contributions

All the authors have participated in writing the manuscript and have revised the final version. All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by GD, SS, AKJ, MS, PS, and MM. The first draft of the manuscript was written by GD, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conceptualization: GD, SS. Methodology: GD, AKJ. Formal analysis and investigation: GD, MS, SS. Writing — original draft preparation: GD, PS, MM. Writing — review and editing: GD, AKJ, MS. Supervision: MM.

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Correspondence to Somya Srivastava.

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Dubey, G., Srivastava, S., Jayswal, A.K. et al. Fetal Ultrasound Segmentation and Measurements Using Appearance and Shape Prior Based Density Regression with Deep CNN and Robust Ellipse Fitting. J Digit Imaging. Inform. med. 37, 247–267 (2024). https://doi.org/10.1007/s10278-023-00908-8

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