Name
Abstract | ||
|---|---|---|
Background and Objectives: Measuring head-circumference (HC) length from ultrasound (US) images is a crucial clinical task to assess fetus growth. To lower intraand 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. (c) 2020 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.cmpb.2020.105771 | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE |
Keywords | DocType | Volume |
Fetal ultrasounds, Head circumference delineation, Regression networks, Convolutional neural networks | Journal | 198 |
ISSN | Citations | PageRank |
0169-2607 | 1 | 0.38 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria Chiara Fiorentino | 1 | 1 | 0.38 |
| Sara Moccia | 2 | 38 | 9.44 |
| Morris Capparuccini | 3 | 1 | 0.38 |
| Sara Giamberini | 4 | 1 | 0.38 |
| Emanuele Frontoni | 5 | 248 | 47.04 |