A Multi-Task Convolutional Neural Network for Renal Tumor Segmentation and Classification Using Multi-Phasic CT Images - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

A Multi-Task Convolutional Neural Network for Renal Tumor Segmentation and Classification Using Multi-Phasic CT Images

Abstract

Accounting for nearly 2% of all adults, renal cell carcinomas are sensitive to laparoscopic partial nephrectomy (LPN) which needs an accurate diagnosis and localization before operation. Faced with various intensity distribution, erratic location, irregular shape, etc, the image classification and semantic segmentation on CT scans of renal tumor are challenges. This paper presents a multi-task network, segmen-tation and classification convolutional neural network (SCNet), for preoperative assessment of renal tumor. Via the combination of two tasks, semantic features are fed to the classification network and classification results give segmentation network feedbacks in return. Besides, a 2-step segmentation strategy is conducted to the segmentation module which improves the result by 2.8%. Our experimental results of classification and segmentation achieve 100% accuracy and 0.882 dice coefficient of tumor region respectively, which are better than the results of a single classification network and segmentation network.
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Dates and versions

hal-02281573 , version 1 (09-09-2019)

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Tan Pan, Guanyu Yang, Chuanxia Wang, Ziwei Lu, Zhongwen Zhou, et al.. A Multi-Task Convolutional Neural Network for Renal Tumor Segmentation and Classification Using Multi-Phasic CT Images. 2019 IEEE International Conference on Image Processing (ICIP), Sep 2019, Taipei, Taiwan. pp.809-813, ⟨10.1109/ICIP.2019.8802924⟩. ⟨hal-02281573⟩
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