Polyp Detection in Colonoscopy Videos by Bootstrapping Via Temporal Consistency

Computer-aided polyp detection during colonoscopy is beneficial to reduce the risk of colorectal cancers. Deep learning techniques have made significant process in natural object detection. However, when applying those fully supervised methods to polyp detection, the performance is greatly depressed by the deficiency of labeled data. In this paper, we propose a novel bootstrapping method for polyp detection in colonoscopy videos by augmenting training data with temporal consistency. For a detection network that is trained on a small set of annotated polyp images, we fine-tune it with new samples selected from the test video itself, in order to more effectively represent the polyp morphology of current video. A strategy of selecting new samples is proposed by considering temporal consistency in the test video. Evaluated on 11954 endoscopic frames of the CVC-ClinicVideoDB dataset, our method yields great improvement on polyp detection for several detection networks, and achieves state-of-the-art performance on the benchmark dataset.

[1]  Ilangko Balasingham,et al.  Automatic Colon Polyp Detection Using Region Based Deep CNN and Post Learning Approaches , 2018, IEEE Access.

[2]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[3]  Lequan Yu,et al.  Integrating Online and Offline 3D Deep Learning for Automated Polyp Detection in Colonoscopy Videos. , 2016, IEEE journal of biomedical and health informatics.

[4]  Fernando Vilariño,et al.  WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians , 2015, Comput. Medical Imaging Graph..

[5]  Sajjad Waheed,et al.  An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features , 2017, Int. J. Biomed. Imaging.

[6]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[7]  Hao Chen,et al.  Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos , 2017, IEEE Journal of Biomedical and Health Informatics.

[8]  Quan Wang,et al.  An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[9]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Nima Tajbakhsh,et al.  Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information , 2016, IEEE Transactions on Medical Imaging.

[11]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[12]  Aymeric Histace,et al.  Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge , 2017, IEEE Transactions on Medical Imaging.

[13]  Zhengrong Liang,et al.  Computer-aided detection and diagnosis of colon polyps with morphological and texture features , 2004, SPIE Medical Imaging.

[14]  Carmen C. Y. Poon,et al.  Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker , 2018, Pattern Recognit..

[15]  Xavier Dray,et al.  Polyp Detection Benchmark in Colonoscopy Videos using GTCreator: A Novel Fully Configurable Tool for Easy and Fast Annotation of Image Databases , 2018 .

[16]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Aymeric Histace,et al.  Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis , 2017, CARE/CLIP@MICCAI.