Extraction of pneumoconiosis region with Residual U-net

It is difficult for radiologists to accurately classify categories of pneumoconiosis on chest radiography due to the complex pattern of lesions. Therefore, we investigated in SegNet, U-net, and Residual U-net, and developed a method for extracting pneumoconiosis region of each category with higher accuracy. In this study, a total of 54 cases of category 0 to category 3 pneumoconiosis published by the ILO etc. were used. Next, under the guidance of a radiologist, the lung field area was extracted for each category, and these were used as teacher images. Then, learning was performed using SegNet, U-net, and Residual U-net, the Jaccard index was calculated, and the accuracy of the extracted pneumoconiosis region was evaluated. In the Jaccard index in Residual U-net, category 0 was 0.98 ± 0.07, category 1 was 0.97 ± 0.04, category 2 was 0.97 ± 0.05, and category 3 was 0.97 ± 0.04. In all categories 0 to 3, the Jaccard index in Residual U-net was higher than this in SegNet and U-net, and there was a statistically significant difference (P