Tracking Abnormalities in Video Capsule Endoscopy via Convolutional Neural Networks by Intra-frame Training
- 1 November 2018
- journal article
- Published by Japanese Society for Artificial Intelligence in Transactions of the Japanese Society for Artificial Intelligence
- Vol. 33 (6), C-I33_1-I33_1
- https://doi.org/10.1527/tjsai.c-i33
Abstract
Tracking precisely of abnormalities in the gastrointestinal tract is useful for preparing sample image sequences on educational training for medical diagnose on endoscopy. While the gastrointestinal wall deforms continuously in an unpredictable manner, however, abnormalities without distinctive features make it difficult to track over continuous frames. To address this problem, the proposed method employs Convolutional neural networks (CNN) for tracking lesion area. Conventionally, CNN for tracking requires a large amount of sample data for preliminary learning. The state-of-arts tracking methods using CNN are premised on preliminary learning on data similar to target images given a large number of correct answer labels. On the other hand, the proposed method are not required preliminary learning using similar data. The image components in the marked region at the starting frame is similar to components at the only same position, but different between them depending on the degree of overlapped area. Furthermore, in the successive frame, the components in the previous region is similar to them in the identified area. Therefore, similarity can be learned in the previous frame, called it as an intra-frame training. This paper describes the method for tracking an abnormal region by using CNN based on training overlap rates between the abnormal region and local scanning one with the same size on the starting intra-frame. Furthermore, network parameters are transformed from training the similar regions on the continuous frame additionally. We demonstrate the efficiency of the proposed approach using eight common types of gastrointestinal abnormality.Keywords
This publication has 20 references indexed in Scilit:
- The Visual Object Tracking VOT2016 Challenge ResultsPublished by Springer Science and Business Media LLC ,2016
- Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual TrackingPublished by Springer Science and Business Media LLC ,2016
- In Defense of Sparse Tracking: Circulant Sparse TrackerPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Learning Spatially Regularized Correlation Filters for Visual TrackingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Tracking-by-Segmentation with Online Gradient Boosting Decision TreePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Fast R-CNNPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Convolutional Features for Correlation Filter Based Visual TrackingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Fully convolutional networks for semantic segmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Return of the Devil in the Details: Delving Deep into Convolutional NetsPublished by British Machine Vision Association and Society for Pattern Recognition ,2014
- Tracking abnormalities in video capsule endoscopy using surrounding features with a triangular constraintPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012