CROSS REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims priority to Chinese Patent Application No. 201811073199.5 filed on Sep. 14, 2018, the entire content of which is incorporated herein by reference.

TECHNICAL FIELD

The present application relates to the field of image processing, and in particular, to a method of determining an emphysema threshold, a method of processing a pulmonary medical image and a computer device.

BACKGROUND

In the medical field, in order to provide intermediate results for the diagnosis of emphysema, pulmonary medical images may be processed via relevant client-side devices to obtain some pulmonary features, such as at least one of parameters including a region where emphysema may be present (hereinafter referred to as a candidate emphysema region), a position parameter and a size of the candidate emphysema region, and an air content of lung. Depending on these pulmonary features, a doctor cannot directly derive the diagnosis result of the disease or the health status of a patient, but these pulmonary features can be used as reference data.

When detecting the pulmonary features, a relevant client-side device needs a user, for example, a clinician or an imaging device operator or the like, to manually input a threshold for determining a candidate emphysema region according to his/her accumulated medical knowledge and experience, then determines the candidate emphysema region in a pulmonary medical image based on the threshold, and processes the candidate emphysema region so that one or more emphysema parameters can be measured.

From the above, the detection of pulmonary features via relevant client-side devices depends on the threshold input by relevant users, and the threshold depends on the users' medical knowledge and experience. Therefore, operations of the relevant client-side devices are highly dependent on the users. When there is a great difference among thresholds that are input by different users, it is likely that the pulmonary features measured via the relevant client-side devices with respect to the same pulmonary medical image are greatly different. Accordingly, the accuracy of detecting the pulmonary features via the relevant client-side devices may be very low.

SUMMARY

The present disclosure provides methods, devices, systems and apparatus for processing a pulmonary medical image, particularly including determining emphysema thresholds.

One aspect of the present disclosure features a method of determining emphysema thresholds for processing a pulmonary medical image, the method including: determining lung lobe regions in the pulmonary medical image; and for each of the lung lobe regions, clustering CT values in the lung lobe region to divide the lung lobe region into a first sub region and a second sub region, and acquiring a CT value corresponding to an intersection of a first CT value distribution function for the first sub region and a second CT value distribution function for the second sub region in the lung lobe region as an emphysema threshold for the lung lobe region.

In some implementations, determining the lung lobe regions in the pulmonary medical image includes: extracting a lung parenchymal region from the pulmonary medical image; dividing the lung parenchymal region into a left lung region and a right lung region; determining lung lobe fissure points in the left lung region and the right lung region; constructing a lung lobe fissure surface from the determined lung lobe fissure points; and determining each of the lung lobe regions in the pulmonary medical image based on the lung lobe fissure surface.

In some examples, determining the lung lobe fissure points in the left lung region and the right lung region includes: performing Gaussian filtering on the left lung region and the right lung region; suppressing at least one of local fissure bright spots, blood vessel wall signal points, fissure points near blood vessel walls, or non-planar and non-curved fissure points in the filtered left lung region and right lung region, to obtain a processed left lung region and right lung region; and extracting the lung lobe fissure points from the processed left lung region and right lung region.

In some implementations, clustering the CT values in the lung lobe region to divide the lung lobe region into the first sub region and the second sub region includes: for each of pixels in the lung lobe region, substituting a CT value of the pixel into a probability density function of a Gaussian Mixture Model to calculate a first probability and a second probability of the pixel, where the first probability represents a probability that the pixel belongs to a predetermined first classification, and the second probability represents a probability that the pixel belongs to a predetermined second classification, and classifying the pixel into a classification indicated by a higher one of the first possibility and the second possibility; determining a region formed by pixels classified into the predetermined first classification as the first sub region of the lung lobe region; and determining a region formed by pixels classified into the predetermined second classification as the second sub region of the lung lobe region.

In some implementations, acquiring the CT value corresponding to the intersection of the first CT value distribution function for the first sub region and the second CT value distribution function for the second sub region in the lung lobe region as the emphysema threshold for the lung lobe region includes: acquiring the first CT value distribution function based on CT values in the first sub region of the lung lobe region and predetermined distribution function values of the CT values; acquiring the second CT value distribution function based on CT values in the second sub region of the lung lobe region and predetermined distribution function values of the CT values; calculating the intersection of the first CT value distribution function and the second CT value distribution function; and determining an abscissa value of the intersection as the emphysema threshold for the lung lobe region.

Both of the first CT value distribution function and the second CT value distribution function can be probability density functions in a Gaussian Mixture Model.

Another aspect of the present disclosure features a method of processing a pulmonary medical image, the method including: determining lung lobe regions in the pulmonary medical image; and for each of the lung lobe regions, acquiring an emphysema threshold for the lung lobe region, where the emphysema threshold is a CT value corresponding to an intersection of a first CT value distribution function for a first sub region of the lung lobe region and a second CT value distribution function for a second sub region of the lung lobe region, the first sub region and the second sub region being obtained by clustering CT values in the lung lobe region, and determining a candidate emphysema region in the lung lobe region according to the emphysema threshold for the lung lobe region.

In some implementations, determining the candidate emphysema region in the lung lobe region according to the emphysema threshold for the lung lobe region includes: determining a part of the lung lobe region having CT values smaller than the emphysema threshold for the lung lobe region as the candidate emphysema region in the lung lobe region.

In some implementations, acquiring the emphysema threshold for the lung lobe region includes: clustering the CT values in the lung lobe region to divide the lung lobe region into the first sub region and the second sub region; and acquiring the CT value corresponding to the intersection of the first CT value distribution function for the first sub region and the second CT value distribution function for the second sub region in the lung lobe region as the emphysema threshold for the lung lobe region.

In some examples, clustering the CT values in the lung lobe region to divide the lung lobe region into the first sub region and the second sub region includes: for each of pixels in the lung lobe region, substituting a CT value of the pixel into a probability density function of a Gaussian Mixture Model to calculate a first probability and a second probability of the pixel, where the first probability represents a probability that the pixel belongs to a predetermined first classification, and the second probability represents a probability that the pixel belongs to a predetermined second classification, and classifying the pixel into a classification indicated by a higher one from the first possibility and the second possibility; determining a region formed by pixels classified into the predetermined first classification as the first sub region of the lung lobe region; and determining a region formed by pixels classified into the predetermined second classification as the second sub region of the lung lobe region.

In some examples, acquiring the CT value corresponding to the intersection of the first CT value distribution function for the first sub region and the second CT value distribution function for the second sub region in the lung lobe region as the emphysema threshold for the lung lobe region includes: acquiring the first CT value distribution function based on CT values in the first sub region of the lung lobe region and predetermined distribution function values of the CT values; acquiring the second CT value distribution function based on CT values in the second sub region of the lung lobe region and predetermined distribution function values of the CT values; calculating the intersection of the first CT value distribution function and the second CT value distribution function; and determining an abscissa value of the intersection as the emphysema threshold for the lung lobe region.

Both of the first CT value distribution function and the second CT value distribution function can be probability density functions in a Gaussian Mixture Model.

In some implementations, the method further includes: optimizing the candidate emphysema region using a pre-trained big data analysis model. The pulmonary medical image can be associated with a region of interest of a subject, and the pre-trained big data analysis model can be trained by samples including a plurality of pulmonary medical images obtained from multiple CT scans of the same region of interest of multiple subjects and corresponding emphysema thresholds for the plurality of pulmonary medical images.

A further aspect of the present disclosure features a computing device for processing a pulmonary medical image, the device including: at least one processor; and at least one non-transitory machine readable storage medium coupled to the at least one processor having machine-executable instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to perform operations including: determining lung lobe regions in the pulmonary medical image; and for each of the lung lobe regions, acquiring an emphysema threshold for the lung lobe region, where the emphysema threshold is a CT value corresponding to an intersection of a first CT value distribution function for a first sub region and a second CT value distribution function for a second sub region in the lung lobe region, and the first sub region and the second sub region are obtained by clustering CT values in the lung lobe region, and determining a candidate emphysema region in the lung lobe region according to the emphysema threshold for the lung lobe region.

In some implementations, determining the candidate emphysema region in the lung lobe region according to the emphysema threshold for the lung lobe region includes: determining a part of the lung lobe region having CT values smaller than the emphysema threshold for the lung lobe region as the candidate emphysema region in the lung lobe region.

In some implementations, acquiring the emphysema threshold for the lung lobe region includes: clustering CT values in the lung lobe region to divide the lung lobe region into the first sub region and the second sub region; and acquiring the CT value corresponding to the intersection of the first CT value distribution function for the first sub region and the second CT value distribution function for the second sub region in the lung lobe region as the emphysema threshold for the lung lobe region.

In some examples, clustering the CT values in the lung lobe region to divide the lung lobe region into the first sub region and the second sub region includes: for each of pixels in the lung lobe region, substituting a CT value of the pixel into a probability density function of a Gaussian Mixture Model to calculate a first probability and a second probability of the pixel, where the first probability represents a probability that the pixel belongs to a predetermined first classification, and the second probability represents a probability that the pixel belongs to a predetermined second classification, and classifying the pixel into a classification indicated by a higher one from the first possibility and the second possibility; determining a region formed by pixels classified into the predetermined first classification as the first sub region of the lung lobe region; and determining a region formed by pixels classified into the predetermined second classification as the second sub region of the lung lobe region.

In some implementations, acquiring the CT value corresponding to the intersection of the first CT value distribution function for the first sub region and the second CT value distribution function for the second sub region in the lung lobe region as the emphysema threshold for the lung lobe region includes: acquiring the first CT value distribution function based on CT values in the first sub region of the lung lobe region and predetermined distribution function values of the CT values; acquiring the second CT value distribution function based on CT values in the second sub region of the lung lobe region and predetermined distribution function values of the CT values; calculating the intersection of the first CT value distribution function and the second CT value distribution function; and determining an abscissa value of the intersection as the emphysema threshold for the lung lobe region.

In some implementations, determining the lung lobe regions in the pulmonary medical image includes: extracting a lung parenchymal region from the pulmonary medical image; dividing the lung parenchymal region into a left lung region and a right lung region; determining lung lobe fissure points in the left lung region and the right lung region; constructing a lung lobe fissure surface from the determined lung lobe fissure points; and determining each of the lung lobe regions in the pulmonary medical image based on the lung lobe fissure surface. Determining the lung lobe fissure points in the left lung region and the right lung region can include: performing Gaussian filtering on the left lung region and the right lung region; suppressing local fissure bright spots, blood vessel wall signal points, fissure points near blood vessel walls, and non-planar and non-curved fissure points in the filtered left lung region and right lung region, to obtain a processed left lung region and right lung region; and extracting the lung lobe fissure points from the processed left lung region and right lung region.

The details of one or more examples of the subject matter described in the present disclosure are set forth in the accompanying drawings and description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims. Features of the present disclosure are illustrated by way of example and not limited in the following figures, in which like numerals indicate like elements.

