Renal tumors segmentation in abdomen CT Images using 3D-CNN and ConvLSTM. (February 2022)

• Record Type: Journal Article
• Authors: Kang, Li; Zhou, Ziqi; Huang, Jianjun; Han, Wenzhong
• Is Part Of: Biomedical signal processing and control. Volume 72(2022)Part B
• Publication Date: 2022-02
• Subjects: Renal tumors -- Segmentation -- Convolutional neural network (CNN) -- Convolutional long short-term memory (ConvLSTM); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103334
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Renal tumor is one of the common tumors with high incidence, and accurate segmentation of renal tumors is helpful for preoperative evaluation. Computed Tomography (CT) plays an important role in the treatment of renal tumors and accurate segmentation of tumors in CT images may provide critical information for surgery. In this paper, a segmentation approach based on deep learning with limited computation cost is proposed to improve the segmentation accuracy for kidneys and renal tumors. Firstly, a pre-trained restruction network is presented to alleviate small samples problems, which utilizes abdominal CT data to transfer network model effectively; Then, prior contour-assisted channel is introduced in two-dimensional network to segment the region of interest which contains kidneys and renal tumors and act as the input of the subsequente fine segmentation network; Finally, convolutional long short-term memory (ConvLSTM) is employed to extract spatial correlation information between slices and combined with a three-dimensional convolutional neural networks for fine segmentation. Several experiments on the 2019 renal tumor segmentation challenge(Kits19) dataset are designed to evaluate the performance of the proposed method, and the mean segmentation accuracy for kidneys and renal tumors are 96.39% and 78.91% for cross validation tests, which outperforms the other neural network algorithms, including 3D Res-Unet with 95.4% and 72.35%.