Maria Chiara Fiorentino1, Sara Moccia2, Morris Capparuccini1, Sara Giamberini1, Emanuele Frontoni1. 1. Department of Information Engineering, Universita Politecnica delle Marche, Via Brecce Bianche, 12, Ancona 60131, Italy. 2. Department of Information Engineering, Universita Politecnica delle Marche, Via Brecce Bianche, 12, Ancona 60131, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Via Morego, 30, Genova 16163, Italy. Electronic address: s.moccia@staff.univpm.it.
Abstract
BACKGROUND AND OBJECTIVES: Measuring head-circumference (HC) length from ultrasound (US) images is a crucial clinical task to assess fetus growth. To lower intra- and inter-operator variability in HC length measuring, several computer-assisted solutions have been proposed in the years. Recently, a large number of deep-learning approaches is addressing the problem of HC delineation through the segmentation of the whole fetal head via convolutional neural networks (CNNs). Since the task is a edge-delineation problem, we propose a different strategy based on regression CNNs. METHODS: The proposed framework consists of a region-proposal CNN for head localization and centering, and a regression CNN for accurately delineate the HC. The first CNN is trained exploiting transfer learning, while we propose a training strategy for the regression CNN based on distance fields. RESULTS: The framework was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. A mean absolute difference of 1.90 ( ± 1.76) mm and a Dice similarity coefficient of 97.75 ( ± 1.32) % were achieved, overcoming approaches in the literature. CONCLUSIONS: The experimental results showed the effectiveness of the proposed framework, proving its potential in supporting clinicians during the clinical practice.
BACKGROUND AND OBJECTIVES: Measuring head-circumference (HC) length from ultrasound (US) images is a crucial clinical task to assess fetus growth. To lower intra- and inter-operator variability in HC length measuring, several computer-assisted solutions have been proposed in the years. Recently, a large number of deep-learning approaches is addressing the problem of HC delineation through the segmentation of the whole fetal head via convolutional neural networks (CNNs). Since the task is a edge-delineation problem, we propose a different strategy based on regression CNNs. METHODS: The proposed framework consists of a region-proposal CNN for head localization and centering, and a regression CNN for accurately delineate the HC. The first CNN is trained exploiting transfer learning, while we propose a training strategy for the regression CNN based on distance fields. RESULTS: The framework was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. A mean absolute difference of 1.90 ( ± 1.76) mm and a Dice similarity coefficient of 97.75 ( ± 1.32) % were achieved, overcoming approaches in the literature. CONCLUSIONS: The experimental results showed the effectiveness of the proposed framework, proving its potential in supporting clinicians during the clinical practice.
Authors: Mariachiara Di Cosmo; Maria Chiara Fiorentino; Francesca Pia Villani; Emanuele Frontoni; Gianluca Smerilli; Emilio Filippucci; Sara Moccia Journal: Med Biol Eng Comput Date: 2022-09-24 Impact factor: 3.079