Combo loss: Handling input and output imbalance in multi-organ segmentation. (July 2019)

• Record Type: Journal Article
• Authors: Taghanaki, Saeid Asgari; Zheng, Yefeng; Kevin Zhou, S.; Georgescu, Bogdan; Sharma, Puneet; Xu, Daguang; Comaniciu, Dorin; Hamarneh, Ghassan
• Is Part Of: Computerized medical imaging and graphics. Volume 75(2019)
• Page Start: 24
• Page End: 33
• Publication Date: 2019-07
• Subjects: Class-imbalance -- Output imbalance -- Deep convolutional neural networks -- Loss function -- Multi-organ segmentation; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2019.04.005
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: A novel loss function for multi-organ segmentation. Handling both input and output class imbalance. Smoothing dice (or similar discrete) loss function(s). Preventing potential gradient vanishing/exploding problem caused by Dice or similar loss functions. First deep network for PET multi-organ segmentation. Tested on various data-sets: MRI, PET, and CT. Abstract: 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. In order to tackle both types of imbalance during training and inference, we introduce a new curriculum learning based loss function. Specifically, we leverage Dice similarity coefficient to deter model parameters from being held at bad local minima and at the same time gradually learn better model parameters by penalizing for false positives/negatives using a cross entropy term. We evaluated the proposed loss function on three datasets: whole body positron emission tomography (PET) scans with 5 target organs, magnetic resonance imaging (MRI) prostate scans, and ultrasound echocardigraphy images with a single target organ i.e., left ventricular. 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.