EP-4738245-A1 - SYSTEM AND METHOD FOR SAGITTAL PLANE CORRECTION OF MEDICAL IMAGES

EP4738245A1EP 4738245 A1EP4738245 A1EP 4738245A1EP-4738245-A1

Abstract

A processing unit configured to reconstruct a shape of a spine structure contained in medical images is provided, segmenting a vertebral body by inputting the medical images to a deep learning model trained in advance; constructing a centerline by connecting central points of segmented vertebral bodies; setting a correction coordinate system based on a sagittal plane by analyzing a curvature of the centerline; generating a sagittal plane cross-section image by transforming the shape of the spine structure based on the correction coordinate system; and outputting the sagittal plane cross-section image as a diagnostic image.

Inventors

KIM, JONG HYO
LEE, Je Myoung
KIM, MIN BEOM

Assignees

Claripi Inc.

Dates

Publication Date: 20260506
Application Date: 20250605

Claims (15)

An apparatus for sagittal plane correction of medical images, the apparatus comprising: a processing unit configured to reconstruct a shape of a spine structure contained in medical images, wherein the processing unit is configured to: segment a vertebral body by inputting the medical images to a deep learning model trained in advance; construct a centerline by connecting central points of segmented vertebral bodies; set a correction coordinate system based on a sagittal plane by analyzing a curvature of the centerline; generate a sagittal plane cross-section image by transforming the shape of the spine structure based on the correction coordinate system; and output the sagittal plane cross-section image as a diagnostic image.
The apparatus of claim 1, wherein the deep learning model incorporates a U-Net or Attention architecture, and performs segmentation for vertebral bodies based on a mask.
The apparatus of claim 2, wherein the processing unit derives each central point of the vertebral body by calculating coordinates of the vertebral body, and constructs a three-dimensional centerline by connecting the central points of the vertebral bodies.
The apparatus of claim 3, wherein the processing unit uses at least one of linear interpolation, a cubic Bézier curve, and a cubic spline to construct the three-dimensional centerline.
The apparatus of claim 3, wherein the processing unit defines a start point and an end point with a straight line in the medical images, and constructs the three-dimensional centerline by calculating an intermediate position after identifying a position between the two points.
The apparatus of claim 3, wherein the processing unit defines a start point and an end point in the medical images, and constructs the three-dimensional centerline by identifying a shape of a curve through control points arranged in a curvature direction or tangential direction.
The apparatus of claim 3, wherein the processing unit connects the central points of the vertebral bodies in the medical images with a curve, and defines a spline curve for each axis of a global coordinate system to construct the three-dimensional centerline.
The apparatus of claim 1, wherein the processing unit calculates a tangent vector and a curvature at a preset point in analyzing the curvature of the centerline, and the preset point comprises at least one of the central points of the vertebral bodies and interpolated points between the vertebral bodies.
The apparatus of claim 8, wherein the processing unit calculates a direction of the centerline based on difference in the central point between the vertebral bodies in calculating the tangent vector.
The apparatus of claim 8, wherein the processing unit calculates a degree to which the centerline is bent, based on difference in the central point between the vertebral bodies in calculating the curvature.
The apparatus of claim 8, wherein the processing unit uses a local coordinate system defined based on the tangent vector and curvature of the centerline in setting the correction coordinate system.
The apparatus of claim 11, wherein the processing unit transforms the medical images according to the correction coordinate system, and reconstructs the medical images into a sagittal plane cross-section image from which the curvature is removed.
The apparatus of claim 1, wherein the medical images comprise computed tomography images of a thorax or lumbar region.
A system for sagittal plane correction of medical images, the system comprising: a communication unit configured to acquire medical images; and a processing unit configured to reconstruct a shape of a spine structure contained in the medical images, wherein the processing unit is configured to: segment a vertebral body by inputting the medical images to a deep learning model trained in advance; construct a centerline by connecting central points of segmented vertebral bodies; set a correction coordinate system based on a sagittal plane by analyzing a curvature of the centerline; generate a sagittal plane cross-section image by transforming the shape of the spine structure based on the correction coordinate system; and output the sagittal plane cross-section image as a diagnostic image.
A method of sagittal plane correction of medical images to reconstruct a shape of a spine structure contained in the medical images, the method comprising: segmenting a vertebral body by inputting the medical images to a deep learning model trained in advance; constructing a centerline by connecting central points of segmented vertebral bodies; setting a correction coordinate system based on a sagittal plane by analyzing a curvature of the centerline; generating a sagittal plane cross-section image by transforming the shape of the spine structure based on the correction coordinate system; and outputting the sagittal plane cross-section image as a diagnostic image.

Description

BACKGROUND OF THE INVENTION Field of the Invention The disclosure relates to a system and method for sagittal plane correction of medical images, and more particularly to a system and method for sagittal plane correction of medical images, in which a spine structure of a scoliosis patient in the medical image is corrected to reconstruct a distorted sagittal plane cross-section into an aligned state. Description of the Related Art In general, a curved planar reformation (CPR) technique is used in the diagnosis and evaluation of scoliosis. A computerized photometric radiographic imaging technique uses a computer-based photogrammetric technique to accurately diagnose and evaluate the spinal deformity of a patient. The computerized photometric radiographic imaging technique is useful for the diagnosis and follow-up of scoliosis because it is non-invasive and allows for three-dimensional modeling and accurate angle measurement based on a computer analysis of captured images. In this computerized photometric radiographic imaging technique, the three-dimensional curvature of a spine is measured by manually specifying the central points of vertebral bodies and generating a curved path. However, in the diagnosis of spinal disorders, it takes a lot of diagnostic time to manually analyze multiple slices, and it is difficult to identify the entire spine at once, thereby limiting the diagnostic efficiency and accuracy. Documents of Related Art] [Patent Document] Korean Patent Publication No. 2024-0042866 (titled "METHOD FOR PROVIDING INFORMATION ON SCOLIOSIS BASED ON ARTIFICIAL INTELLIGENCE," and published on April 2, 2024). SUMMARY OF THE INVENTION An aspect of the disclosure is to provide a system and method for sagittal plane correction of medical images, in which a sagittal plane image is automatically reconstructed by correcting the curvature of a spine from the medical images of a scoliosis patient based on a deep learning model, thereby intuitively displaying the entire spine structure. In accordance with an embodiment of the disclosure, an apparatus for sagittal plane correction of medical images includes: a processing unit configured to reconstruct a shape of a spine structure contained in medical images, wherein the processing unit is configured to: segment a vertebral body by inputting the medical images to a deep learning model trained in advance; construct a centerline by connecting central points of segmented vertebral bodies; set a correction coordinate system based on a sagittal plane by analyzing a curvature of the centerline; generate a sagittal plane cross-section image by transforming the shape of the spine structure based on the correction coordinate system; and output the sagittal plane cross-section image as a diagnostic image. The deep learning model may incorporate a U-Net or Attention architecture, and perform segmentation for vertebral bodies based on a mask. The processing unit may derive each central point of the vertebral body by calculating coordinates of the vertebral body, and construct a three-dimensional centerline by connecting the central points of the vertebral bodies. The processing unit may use at least one of linear interpolation, a cubic Bézier curve, and a cubic spline to construct the three-dimensional centerline. The processing unit may define a start point and an end point with a straight line in the medical images, and construct the three-dimensional centerline by calculating an intermediate position after identifying a position between the two points. The processing unit may define a start point and an end point in the medical images, and construct the three-dimensional centerline by identifying a shape of a curve through control points arranged in a curvature direction or tangential direction. The processing unit may connect the central points of the vertebral bodies in the medical images with a curve, and define a spline curve for each axis of a global coordinate system to construct the three-dimensional centerline. The processing unit may calculate a tangent vector and a curvature at a preset point in analyzing the curvature of the centerline, and the preset point may include at least one of the central points of the vertebral bodies and interpolated points between the vertebral bodies. The processing unit may calculate a direction of the centerline based on difference in the central point between the vertebral bodies in calculating the tangent vector. The processing unit may calculate a degree to which the centerline is bent, based on difference in the central point between the vertebral bodies in calculating the curvature. The processing unit may use a local coordinate system defined based on the tangent vector and curvature of the centerline in setting the correction coordinate system. The processing unit may transform the medical images according to the correction coordinate system, and reconstruct the medical images into a sagittal plane cross-section image from wh