The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W4, 13–17, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W4-13-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W4, 13–17, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W4-13-2017

  10 May 2017

10 May 2017

LESION DETECTION IN CT IMAGES USING DEEP LEARNING SEMANTIC SEGMENTATION TECHNIQUE

A. Kalinovsky1, V. Liauchuk1, and A. Tarasau2 A. Kalinovsky et al.
  • 1United Institute of Informatics, Minsk, Belarus
  • 2Scientific and Practical Center for Pulmonology and Tuberculosis, Minsk, Belarus

Keywords: Deep Neural Networks, Convolutional Neural Networks, Semantic Segmentation, Chest Radiography, Tuberculosis

Abstract. In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.