Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic Ct Images

Dong Liang, Ruofeng Tong, Jian Wu, Lanfen Lin, Xiao Chen, Hongjie Hu, Qiaowei Zhang, Qingqing Chen, Yutaro Iwamoto, Xianhua Han, Yen-Wei Chen

Abstract: Convolutional neural networks (CNNs) have achieved great success in numerous challenging vision tasks, and have great potential for object detection in natural images. Compared with the natural images, medical images exhibit some unique characteristics. Therefore, substantial challenges still remain in this field. The first challenge is to develop a method for effectively distilling enhancement patterns from the dynamic CT images. Moreover, since tumor sizes vary greatly and small lesions are important for early liver tumor detection, lesion detection with a widely variable scale is another challenge. In this paper, we propose a multi-stream scale-insensitive convolutional and recurrent neural network (MSCR) for liver tumor detection. Specifically, we propose the use of grouped convolutional long short-term memory (GCLSTM) to extract enhancement patterns, which is developed as a plug-and-play module. Experiments show that the MSCR framework exhibits superior performance over state-of-the-art approaches, achieving an average precision of 77.06% for detection of focal liver lesions. We have released the code of MSCR in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .