US 12,471,798 B2
Sacroiliitis discrimination method using sacroiliac joint MR image
Seulkee Lee, Seoul (KR); Hoon-Suk Cha, Seoul (KR); Uju Jeon, Seoul (KR); and Myung Jin Chung, Seoul (KR)
Assigned to SAMSUNG LIFE PUBLIC WELFARE FOUNDATION, Seoul (KR)
Filed by SAMSUNG LIFE PUBLIC WELFARE FOUNDATION, Seoul (KR)
Filed on Dec. 21, 2023, as Appl. No. 18/392,475.
Claims priority of application No. 10-2023-0040767 (KR), filed on Mar. 28, 2023.
Prior Publication US 2024/0324891 A1, Oct. 3, 2024
Int. Cl. A61B 5/055 (2006.01); G06T 7/00 (2017.01); G16H 30/40 (2018.01)
CPC A61B 5/055 (2013.01) [G06T 7/0012 (2013.01); G16H 30/40 (2018.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30008 (2013.01); G06T 2207/30012 (2013.01)] 7 Claims
OG exemplary drawing
 
1. A method for discriminating sacroiliitis by using sacroiliac joint MR images through enhanced computer-based image processing techniques, implemented by one or more processors of a computing device, the method comprising the steps of:
collecting MR images related to a sacroiliac joint of a patient using a medical imaging system;
preprocessing the collected MR images;
extracting a region of interest (ROI) corresponding to a sacroiliac joint from the preprocessed MR images by using a trained object detection model comprising a Faster Region-based Convolutional Neural Network (Faster R-CNN), wherein the model is trained to detect anatomical boundaries of the sacrum and ilium;
resizing the extracted ROI to a predefined size;
augmenting the ROI images labeled as positive-class training data using one or more of rotation, blurring, sharpening, contrast adjustment, or noise addition;
generating training data by stacking at least three consecutive MR slices including the ROI;
training a binary classification model comprising a VGG-19-based neural network using the training data; and
using the trained model to determine the presence or absence of sacroiliitis in new MR images of a patient,
wherein the step of preprocessing comprises a step of normalizing the collected MR images to minimize intensity variations in the MR images due to brightness variability between images of different samples or within slices of the samples,
wherein the step of normalizing MR images comprises:
stacking the MR images into a 3D volume;
dividing the 3D volume into a plurality of grid regions;
applying adaptive histogram equalization to each grid region to normalize brightness variations between slices;
converting the processed volume into 2D slices with enhanced contrast and uniform brightness.