Regressing Heatmaps for Multiple Landmark Localization Using CNNs

Christian Payer, Darko Stern, Horst Bischof, Martin Urschler

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


We explore the applicability of deep convolutional neural networks (CNNs) for multiple landmark localization in medical image data. Exploiting the idea of regressing heatmaps for individual landmark locations, we investigate several fully convolutional 2D and 3D CNN architectures by training them in an end-to-end manner. We further propose a novel SpatialConfiguration-Net architecture that effectively combines accurate local appearance responses with spatial landmark configurations that model anatomical variation. Evaluation of our different architectures on 2D and 3D hand image datasets show that heatmap regression based on CNNs achieves state-of-the-art landmark localization performance, with SpatialConfiguration-Net being robust even in case of limited amounts of training data.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2016
Subtitle of host publication19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II
EditorsSebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells
PublisherSpringer International Publishing AG
Number of pages9
ISBN (Electronic)978-3-319-46723-8
ISBN (Print)978-3-319-46722-1
Publication statusPublished - 21 Oct 2016
Event19th International Conference on Medical Image Computing & Computer Assisted Intervention: MICCAI 2016 - Intercontinental Athenaeum, Athens, Greece
Duration: 17 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science


Conference19th International Conference on Medical Image Computing & Computer Assisted Intervention
Abbreviated titleMICCAI
Internet address

Fields of Expertise

  • Information, Communication & Computing


  • BioTechMed-Graz

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