Fast Automatic Bone Surface Segmentation in Ultrasound Images Without Machine Learning

Shihfan Jack Tu, Jules Morel, Minsi Chen, Stephen J. Mellon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Reconstructing 3D bone images with 2D clinical ultrasound image is one of the primary developmental trends of computer-assisted orthopaedic surgery procedures, and real-time bone segmentation is required for such development. We previously presented a dynamic programming method with local phase tensor extraction for bone structure segmentation that could process one ultrasound frame with a true positive ratio of 71% in approximately 1 s. The present study aimed to reduce the segmentation time to enable real-time computational capacity for clinical application developments. A simplified bone probability algorithm was optimised by systematically identifying and removing the components which cost most computing resources. The segmentation results produced by the bone probability method were compared to the local phase method, and manual segmentation carried out by clinical experts. The proposed method had higher recall metric (0.67) than the local phase method (0.61), while the computational time is reduced to 0.02 s per image. However, the bone probability method did not perform as well as the local phase method in specificity and precision metrics. In conclusion, the simplified version of the segmentation algorithm improved computational speed and promised an advantage in further real time application developments, but additional functions that can improve accuracy and further extensive validations are still required before further clinical application developments.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis
Subtitle of host publication25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12–14, 2021, Proceedings
EditorsBartłomiej W. Papież, Mohammad Yaqub, Jianbo Jiao, Ana I. L. Namburete, J. Alison Noble
PublisherSpringer Nature Switzerland AG
Pages250-264
Number of pages15
VolumeLNCS 12722
Edition1st
ISBN (Electronic)9783030804329
ISBN (Print)9783030804312
DOIs
Publication statusPublished - 6 Jul 2021
Event25th Annual Conference on Medical Image Understanding and Analysis - Virtual, Online
Duration: 12 Jul 202114 Jul 2021
Conference number: 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12722 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th Annual Conference on Medical Image Understanding and Analysis
Abbreviated titleMIUA 2021
CityVirtual, Online
Period12/07/2114/07/21

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