Learning-Based Shape Model Matching: Training Accurate Models with Minimal Manual Input

C Lindner, J Thomson, The ArcOGEN Consortium, TF Cootes, N Navab (Editor), J Hornegger (Editor), WM Wells (Editor), AF Frangi (Editor)

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

    131 Downloads (Pure)

    Abstract

    Recent work has shown that statistical model-based methods lead to accurate and robust results when applied to the segmentation of bone shapes from radiographs. To achieve good performance, model-based matching systems require large numbers of annotations, which can be very time-consuming to obtain. Non-rigid registration can be applied to unlabelled images to obtain correspondences from which models can be built. However, such models are rarely as effective as those built from careful manual annotations, and the accuracy of the registration is hard to measure. In this paper, we show that small numbers of manually annotated points can be used to guide the registration, leading to significant improvements in performance of the resulting model matching system, and achieving results close to those of a model built from dense manual annotations. Placing such sparse points manually is much less time-consuming than a full dense annotation, allowing good models to be built for new bone shapes more quickly than before. We describe detailed experiments on varying the number of sparse points, and demonstrate that manually annotating fewer than 30% of the points is sufficient to create robust and accurate models for segmenting hip and knee bones in radiographs. The proposed method includes a very effective and novel way of estimating registration accuracy in the absence of ground truth.
    Original languageEnglish
    Title of host publicationThe 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2015, Part III)
    EditorsN Navab, J Hornegger, WM Wells, AF Frangi
    PublisherSpringer Nature
    Pages580-587
    Number of pages8
    Volume9351
    DOIs
    Publication statusPublished - 2015
    EventInternational Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) - Nice, France
    Duration: 1 Jan 1824 → …

    Publication series

    NameLecture Notes in Computer Science

    Conference

    ConferenceInternational Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
    CityNice, France
    Period1/01/24 → …

    Fingerprint

    Dive into the research topics of 'Learning-Based Shape Model Matching: Training Accurate Models with Minimal Manual Input'. Together they form a unique fingerprint.

    Cite this