3 years ago

Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation.

Saeid Asgari Taghanaki, Daguang Xu, Puneet Sharma, Yefeng Zheng, S. Kevin Zhou, Dorin Comaniciu, Ghassan Hamarneh, Bogdan Georgescu

Simultaneous segmentation of multiple organs from different medical imaging modalities is a crucial task as it can be utilized for computer-aided diagnosis, computer-assisted surgery, and therapy planning. Thanks to the recent advances in deep learning, several deep neural networks for medical image segmentation have been introduced successfully for this purpose. In this paper, we focus on learning a deep multi-organ segmentation network that labels voxels. In particular, we examine the critical choice of a loss function in order to handle the notorious imbalance problem that plagues both the input and output of a learning model. The input imbalance refers to the class-imbalance in the input training samples (i.e. small foreground objects embedded in an abundance of background voxels, as well as organs of varying sizes). The output imbalance refers to the imbalance between the false positives and false negatives of the inference model. We introduce a loss function that integrates a weighted cross-entropy with a Dice similarity coefficient to tackle both types of imbalance during training and inference. We evaluated the proposed loss function on three datasets of whole body PET scans with 5 target organs, MRI prostate scans, and ultrasound echocardigraphy images with a single target organ. We show that a simple network architecture with the proposed integrative loss function can outperform state-of-the-art methods and results of the competing methods can be improved when our proposed loss is used.

Publisher URL: http://arxiv.org/abs/1805.02798

DOI: arXiv:1805.02798v3

You might also like
Never Miss Important Research

Researcher is an app designed by academics, for academics. Create a personalised feed in two minutes.
Choose from over 15,000 academics journals covering ten research areas then let Researcher deliver you papers tailored to your interests each day.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.