Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Three-Dimensional Cranio-Facial Landmark Detection in Series of CT Slices, Using Multi-Phased Regression Networks

Version 1 : Received: 7 April 2023 / Approved: 10 April 2023 / Online: 10 April 2023 (07:59:58 CEST)

A peer-reviewed article of this Preprint also exists.

Nishimoto, S.; Saito, T.; Ishise, H.; Fujiwara, T.; Kawai, K.; Kakibuchi, M. Three-Dimensional Craniofacial Landmark Detection in Series of CT Slices Using Multi-Phased Regression Networks. Diagnostics 2023, 13, 1930. Nishimoto, S.; Saito, T.; Ishise, H.; Fujiwara, T.; Kawai, K.; Kakibuchi, M. Three-Dimensional Craniofacial Landmark Detection in Series of CT Slices Using Multi-Phased Regression Networks. Diagnostics 2023, 13, 1930.

Abstract

Geometrical assessments of human skull have been done based on the anatomical landmarks. Automatic detection of the landmarks, if developed, will be a great help not only medically, but also anthropologically. An automated system with multi-phased deep learning networks to predict three-dimensional coordinate values of cranio-facial landmarks, was developed. From a publicly available database, computed tomography images of craniofacial area were obtained. They were digitally reconstructed into three-dimensional models. Sixteen anatomical landmarks were plotted on each of the models and coordinate values of them were recorded. Three-phased regression deep learning networks were trained respectively with 90 training datasets. For evaluation, 30 testing datasets were employed. Three-dimensional error for the first phase, testing 30 data, was 11.60 pixels in average. (1 pixel = 500 / 512 mm) For the second phase, it was significantly improved to 4.66 pixels. For the third phase, it was significantly progressed to 2.88. This was comparable to the gaps between the landmarks, plotted by two experienced practitioners. Our proposing method of multi-phased prediction, coarse detection first and narrowing down the detection area, may be a possible solution, within the physical limitation of memory and computation.

Keywords

Multi-phased deep learning; Regression neural network; Coordinate value; Computer assisted tomography (CT); Cranio-facial bone

Subject

Medicine and Pharmacology, Dentistry and Oral Surgery

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.