Abstract: A deep learning-based lung disease diagnosis assistance system according to an embodiment of the present disclosure includes an image input unit inputting a diagnosis target image obtained by capturing a lung image; a bone area removal unit removing a bone area from the diagnosis target image to output a soft tissue image from which the bone area is removed, on the basis of the bone binary model; a lung area extraction unit extracting a lung area from the soft tissue image to output a lung image of the lung area on the basis of a lung segmentation model; and a lung disease diagnosis unit diagnosing whether lung disease is present in the lung image on the basis of a lung disease detection model.

Abstract: An under-sampling apparatus for MR image reconstruction by using machine learning and a method thereof, an MR image reconstruction device by using machine learning and a method thereof, and a recoding medium thereof are disclosed. The disclosed under-smapling apparatus includes: a setting portion that sets a region corresponding to a center of the k-space image as a first region and remaining regions as a second region; and an under-sampling portion that full-samples the first region and under-samples the second region, wherein in the under-sampling performed in the second region, lines are selected at regular intervals and then only the selected line is full-sampled. According to the under-sampling apparatus, a high-resolution MR image can be acquired while reducing imaing time.

Abstract: An under-sampling apparatus for MR image reconstruction by using machine learning and a method thereof, an MR image reconstruction device by using machine learning and a method thereof, and a recoding medium thereof are disclosed. The disclosed under-smapling apparatus includes: a setting portion that sets a region corresponding to a center of the k-space image as a first region and remaining regions as a second region; and an under-sampling portion that full-samples the first region and under-samples the second region, wherein in the under-sampling performed in the second region, lines are selected at regular intervals and then only the selected line is full-sampled. According to the under-sampling apparatus, a high-resolution MR image can be acquired while reducing imaing time.