A Hybrid Multi-atrous and Multi-scale Network for Liver Lesion Detection

Abstract

Liver lesion detection on abdominal computed tomography (CT) is a challenging topic because of its large variance. Current detection methods based on a 2D convolutional neural network (CNN) are limited by the inconsistent view of lesions. One obvious observation is that it can easily lead to a discontinuity problem since it ignores the information between CT slices. To solve this problem, we propose a novel hybrid multi-atrous and multi-scale network (HMMNet). Our network treats the liver lesion detection in a 3D setting as finding a 3D cubic bounding box of a liver lesion. In our work, a multi-atrous 3D convolutional network (MA3DNet) is designed as the backbone. It comes with different dilation rate along z-axis to tackle the various resolutions in z-axis for different CT volumes. In addition, multi-scale features are extracted in a component, called feature extractor, to cover the volume and appearance diversities of liver lesions in a transversal plane. Finally, the features from our backbone and feature extractor are combined to offer the sizing and position measures of liver lesions. These information are frequently referred in a diagnostic report. Compared with other state-of-the-art 2D and 3D convolutional detection models, our HMMNet achieves the top-notch detection performance on the public Liver Tumor Segmentation Challenge (LiTS) dataset, where the F-score are 54.8% and 34.2% on average with the intersection-over-union (IoU) of 0.5 and 0.75 respectively. We also notice that our HMMNet model can be directly applied to the public Medical Segmentation Decathlon dataset without fine-tuning. This further illustrates the generalization capability of our proposed method.