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Title:
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DEEP LEARNING BASED MODEL FOR AMNIOTIC FLUID SEGMENTATION IN 2D FETAL ULTRASOUND IMAGES |
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Author(s):
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Rihem Mahmoud, Taha Reheh and Selma Belgacem |
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ISBN:
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978-989-8704-53-5 |
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Editors:
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Paula Miranda and Pedro Isaías |
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Year:
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2023 |
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Edition:
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Single |
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Keywords:
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Ultrasound Images, Semantic Segmentation, AF-Pocket, Amniotic Fluid, U-Net |
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Type:
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Full |
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First Page:
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57 |
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Last Page:
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64 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Automatic computerized segmentation of fetal anatomical structures from ultrasound images and biometric measurements is still challenging, due to the inherent characteristics of fetal ultrasound images at different semesters of pregnancy. Generally, clinicians manually measure the fetal anatomical structures by fitting either an ellipse (AF-Pocket plane or a line (for the case of femoral plane), and it can take considerable time to take these measurements. The difficulty in automated fetal anatomical structures measurement stems from various uncertain factors, including poor and non-uniform contrast across the border of the fetal structures, the high variability of the ultrasound images, and large intersect variation. It is extremely difficult to extract the edge of the fetal anatomical structures using local patterns alone. In this paper, we propose an automated method for AF-Pocket semantic segmentation using deep learning methods. The proposed method contains four steps: Pre-processing, binary classification, semantic segmentation and AF-Pocket measurement. The results of our proposed architecture were evaluated using a prepared dataset and achieves the best results compared with a variety of state-of-the-art deep learning-based approaches. |
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