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2018 | OriginalPaper | Chapter

Handling Missing Annotations for Semantic Segmentation with Deep ConvNets

Authors : Olivier Petit, Nicolas Thome, Arnaud Charnoz, Alexandre Hostettler, Luc Soler

Published in: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Publisher: Springer International Publishing

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Abstract

Annotation of medical images for semantic segmentation is a very time consuming and difficult task. Moreover, clinical experts often focus on specific anatomical structures and thus, produce partially annotated images. In this paper, we introduce SMILE, a new deep convolutional neural network which addresses the issue of learning with incomplete ground truth. SMILE aims to identify ambiguous labels in order to ignore them during training, and don’t propagate incorrect or noisy information. A second contribution is SMILEr which uses SMILE as initialization for automatically relabeling missing annotations, using a curriculum strategy. Experiments on 3 organ classes (liver, stomach, pancreas) show the relevance of the proposed approach for semantic segmentation: with 70% of missing annotations, SMILEr performs similarly as a baseline trained with complete ground truth annotations.

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previous chapter Deep Semi-supervised Segmentation with Weight-Averaged Consistency Targets

next chapter A Unified Framework Integrating Recurrent Fully-Convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data

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We drop the dependence of class in \(y_{i,t}^*\) for clarity.

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Title: Handling Missing Annotations for Semantic Segmentation with Deep ConvNets
Authors: Olivier Petit
Nicolas Thome
Arnaud Charnoz
Alexandre Hostettler
Luc Soler
Publisher: Springer International Publishing
Book: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
Print ISBN: 978-3-030-00888-8

Electronic ISBN: 978-3-030-00889-5

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00889-5_3

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