Adaptable landmark localisation: Applying model transfer learning to a shape model matching system

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We address the challenge of model transfer learning for a shape model matching (SMM) system. The goal is to adapt an existing SMM system to work effectively with new data without rebuilding the system from scratch. Recently, several SMM systems have been proposed that combine the outcome of a Random Forest (RF) regression step with shape constraints. These methods have been shown to lead to accurate and robust results when applied to the localisation of landmarks annotating skeletal structures in radiographs. However, as these methods contain a supervised learning component, their performance heavily depends on the data that was used to train the system, limiting their applicability to a new dataset with different properties. Here we show how to tune an existing SMM system by both updating the RFs with new samples and re-estimating the shape model. We demonstrate the effectiveness of tuning a cephalometric SMM system to replicate the annotation style of a new observer. Our results demonstrate that tuning an existing system leads to significant improvements in performance on new data, up to the extent of performing a well as a system that was fully rebuilt using samples from the new dataset. The proposed approach is fast and does not require access to the original training data.

Bibliographical metadata

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
PublisherSpringer-Verlag France
Pages144-151
Number of pages8
Volume10433 LNCS
ISBN (Print)9783319661810
DOIs
StatePublished - Sep 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Event duration: 11 Sep 201713 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10433 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period11/09/1713/09/17