CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No. 201811125405.2, filed on Sep. 26, 2018, and Chinese Patent Application No. 201811126495.7, filed on Sep. 26, 2018, the contents of each of which are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the field of medical image processing, and in particular, to systems and methods for accessing a condition of an organ or tissue.

BACKGROUND

At present, the proportion of old people of 65 years old and above in China is close to 10%, and China becomes one of the countries with a fast growth rate of aging in the world. The aging process is accompanied by a series of changes in organs or tissues. For example, the function of the brain is mainly manifested by brain atrophy, of which brain images show that the brain tissue structure shrinks, the brain parenchyma decreases, the brain weight decreases, the number (or count) of brain cells decreases, the gyrus flattens, the sulcus broadens and deepens, the ventricle, brain cistern, and subarachnoid space expands, and/or indicate mild gliosis and/or different degrees of cerebral artery degenerative lesions. Exemplary clinical manifestations of brain atrophy include memory deterioration, mood instability, thinking ability decreasing, and/or low attention and concentration. In severe occasions, dementia is developed, and the clinical manifestation includes loss of intelligence. The cause of aging is quite complicated, and has not yet been fully clarified, which has greatly affected the life quality of the old people.

Alzheimer's Disease (AD) is also known as senile dementia. In clinical diagnosis, if a patient's brain atrophies to a certain extent and is diagnosed as AD by scales, the patient mostly has reached an untreatable stage. If early diagnosis of AD is achieved, a conversion from brain atrophy to AD can be delayed by medication.

Traditional brain atrophy assessing and diagnosis of old people is mainly performed according to the following steps: (1) data acquisition, (2) data preprocessing, (3) identification by doctor experience, (4) scale test, and (5) diagnosis. The data acquisition refers to a process of acquiring brain images through an imaging device. The data preprocessing refers to a series of operations (e.g., resampling, orientation adjustment of sampled images, skull removing, gray correction, etc.) for reducing or eliminating the impact of the working condition of the imaging device on the quality of the brain images and providing relatively high quality images for subsequent analysis. The identification by doctor experience refers to that the doctor makes an initial diagnosis, based on the brain images, by observing statuses of the brain atrophy, white matter deformation and/or cerebral infarction. The scale test refers to a cognitive assessment through multi-scales based on a preliminary diagnosis result of the brain images, which is mainly performed through question inquiry and takes about 2˜3 hours. Thereafter, a final diagnosis result may be determined based on a combination of a score of the scale test and the preliminary diagnosis result of the brain images.

Traditional methods of the diagnosis of organ or tissue abnormalities (e.g., the brain atrophy) of old people have obtained certain achievements. However, the following problems still exist: first, the diagnosis depends on the doctor's subjective judgment (specifically, descriptions of family members of the patient, cognitive assessment of the patient (e.g., scale test), and the observation of relevant organ or tissue images by the doctor). The method lacks quantitative indicators in diagnosis and is dependent on the doctor's prior knowledge. Second, in the observation and diagnosis of organ or tissue images, even if relevant quantitative morphological characteristics are available, there is no reference standard of normal people of a corresponding age, and the method still relies on experiences of the doctor. At last, with the development of medical devices, the acquisition of organ or tissue images has become more convenient, but quantitative comparison between images of the patient acquired at different times is difficult, and thus the follow-up data of the images cannot be used effectively. Accordingly, it is difficult to analyze a variation trend of the organ or tissue using traditional methods.

Therefore, it is desirable to provide a system, an apparatus and a computer readable storage medium for medical image processing. On the one hand, the systems and methods can quantitatively assess a condition of the organ or tissue of the patient based on images of a corresponding organ or tissue of normal people. On the other hand, the systems and methods can assess the variation trend of the organ or tissue of the patient based on the follow-up data of the organ or tissue images of the patient.

SUMMARY

In the present disclosure, a large number of organ or tissue images of normal people may be acquired. A sub-region may be segmented from these images, and the same sub-region may be segmented from a target image of a target object. The condition of the organ or tissue of the target object may be assessed by comparing a morphological characteristic value of the sub-region of the normal people with that of the sub-region of the target object.

In one aspect of the present disclosure, a method for assessing a condition of an organ or tissue of a target object is provided. The method may include: obtaining a target image of the target object; segmenting a target region from the target image, the target region of the target image corresponding to a sub-region of the organ or tissue; determining a morphological characteristic value of the target region in the target image; obtaining a reference standard associated with a sample organ or tissue of a plurality of sample objects, the sample organ or tissue being of a same type as the organ or tissue of the target object; and/or assessing the condition of the organ or tissue of the target object by comparing the morphological characteristic value of the target region in the target image with the reference standard.

In some embodiments, the target image may be a magnetic resonance (MR) image. In some embodiments, the target object may have an Alzheimer's disease.

In some embodiments, the segmenting a target region from the target image may include: obtaining a target segmentation model; and/or segmenting, using the target segmentation model, the target region from the target image.

In some embodiments, the target segmentation model may include a target artificial intelligence model. In some embodiments, the target artificial intelligence model may include a trained deep learning model.

In some embodiments, the obtaining a target artificial intelligence model may include: retrieving the target artificial intelligence model from a storage device.

In some embodiments, the obtaining a target artificial intelligence model may include: obtaining the target artificial intelligence model by training, using a plurality of training images associated with a second sample organ or tissue of at least one second sample object, an initial artificial intelligence model. The second sample organ or tissue may be of a same type as the organ or tissue of the target object.

In some embodiments, the target artificial intelligence model may be produced according to a process. The process may include: obtaining a plurality of training images associated with a second sample organ or tissue of at least one second sample object, the second sample organ or tissue being of a same type as the organ or tissue of the target object; obtaining an initial artificial intelligence model; and/or determining the target artificial intelligence model by training the initial artificial intelligence model using the plurality of training images.

In some embodiments, the segmenting a target region from the target image may include: segmenting, using a template matching algorithm, the target region from the target image.

In some embodiments, the morphological characteristic value may include at least one of a volume of the organ or tissue, a volume of the target region, a thickness of the target region, and/or a surface area of the target region.

In some embodiments, the organ or tissue may include a brain, and the morphological characteristic value may include at least one of a volume of the brain, a volume of the grey matter of the brain, a volume of the white matter of the brain, a volume of the putamen of the brain, a cortical thickness, or a cortex area.

In some embodiments, the reference standard may relate to a plurality of morphological characteristic values of sample regions in a plurality of sample images of the plurality of sample objects, the sample regions corresponding to the sub-region of the organ or tissue.

In some embodiments, the assessing the condition of the organ or tissue of the target object may include: comparing the morphological characteristic value of the target region with a first portion of the plurality of morphological characteristic values of a first portion of the plurality of sample images corresponding to a first portion of the plurality of sample objects.

In some embodiments, the target object and the first portion of the plurality of sample objects may be of a substantially same or similar age when the target image and the first portion of the plurality of sample images are acquired, respectively. In some embodiments, the target object and the first portion of the plurality of sample objects may be of a same gender.

In some embodiments, the assessing the condition of the organ or tissue of the target object may include: determining a first ranking of the morphological characteristic value of the target region among the first portion of the plurality of morphological characteristic values; and/or assessing the condition of the organ or tissue of the target object based on the first ranking.

In some embodiments, the assessing the condition of the organ or tissue of the target object may include: determining, based on age of the sample object in each of the plurality of sample images when the each sample image is acquired, a second ranking of the morphological characteristic values of the sample regions in the plurality of sample images; determining, based on the second ranking corresponding to the each age, at least one grade of the morphological characteristic values corresponding to the each age; determining, based on the at least one grade, a third ranking of the morphological characteristic value of the target region in the target image among a second portion of the morphological characteristic values of a second portion of the plurality of sample images corresponding to a second portion of the plurality of sample objects; and/or assessing the condition of the organ or tissue of the target object based on the third ranking. The target object and the second portion of the plurality of sample objects may be of a substantially same or similar age when the target image and the second portion of the plurality of sample images are acquired, respectively.

In some embodiments, the assessing the condition of the organ or tissue of the target object may include: determining a relation of the morphological characteristic values corresponding to the plurality of sample objects and age of each of the plurality of sample objects when the each of the plurality of sample images is acquired; and/or determining a grade of the morphological characteristic value corresponding to the target object among a third portion of the morphological characteristic values of a third portion of the plurality of sample images that correspond to a third portion of the plurality of sample objects, based on age of the target object, the morphological characteristic value of the target object, and the relation. The target object and the third portion of the plurality of sample objects may be of a substantially same or similar age when the target image and the third portion of the plurality of sample images are acquired, respectively.

In some embodiments, the determining a relation of the morphological characteristic values corresponding to the plurality of sample objects and age of each of the plurality of sample objects when the each of the plurality of sample images is acquired may include: fitting out a curve representing the relation of the morphological characteristic values corresponding to the plurality of sample objects and the age of the each of the plurality of sample objects when the each of the plurality of sample images is acquired.

In some embodiments, the plurality of morphological characteristic values corresponding to the plurality of sample objects are produced according to a process. The process may include: obtaining the plurality of sample images; for each of the plurality of sample images, segmenting, from the each sample image, a sample region corresponding to the sub-region of the organ or tissue; and/or determining a morphological characteristic value of the sample region in the each sample image.

In some embodiments, the reference standard may include one or more relations relating to a plurality of morphological characteristic values of sample regions in a plurality of sample images of the plurality of sample objects, the sample regions corresponding to the sub-region of the organ or tissue.

In some embodiments, the one or more relations may include at least one first relation associated with one or more sample objects that have a normal condition in the sample organ or tissue.

In some embodiments, the one or more relations may further include at least one second relation associated with one or more sample objects that have an abnormal condition in the sample organ or tissue.

In some embodiments, the one or more relations may be produced according to a process. The process may include: obtaining the plurality of sample images; obtaining age of each of the plurality of sample objects when the each of the plurality of sample images is acquired; segmenting a sample region in each of the plurality of sample images, the sample region being corresponding to the sub-region of the organ or tissue; determining a morphological characteristic value of the sample region in the each sample image; and/or determining a relation between the morphological characteristic values corresponding to the plurality of sample objects and the age of the each of the plurality of sample objects when the each of the plurality of sample images is acquired.

In some embodiments, the method may further include: obtaining a second target image of the target object, the target image and the second target image being acquired at different ages; segmenting, from the second target image, a second target region corresponding to the sub-region of the organ or tissue; determining a second morphological characteristic value of the second target region in the second target image; and/or determining a target variation trend of the morphological characteristic value corresponding to the target object, based on the morphological characteristic value and the second morphological characteristic value corresponding to the target object.

In some embodiments, the method may further include: obtaining a reference variation trend associated with the sample organ or tissue of at least a portion of the plurality of sample objects; and/or assessing the condition of the organ or tissue of the target object by comparing the target variation trend and the reference variation trend.

In another aspect of the present disclosure, a system for assessing a condition of an organ or tissue of a target object is provided. The system may include at least one storage device storing a set of instructions; and/or at least one processor in communication with the storage device. When executing the set of instructions, the at least one processor may be configured to cause the system to perform one or more operations illustrated above.

In another aspect of the present disclosure, a non-transitory computer readable medium storing instructions is provided. The instructions, when executed by at least one processor, may cause the at least one processor to implement the method illustrated above.

In another aspect of the present disclosure, a system for medical image processing is provided. The system may include: a sample data acquisition module configured to acquire a plurality of sample images and age of a sample object in each sample image of the plurality of sample images; a target data acquisition module configured to acquire a target image of a target object and age of the target object; an image segmentation module configured to segment, from the each sample image, a first sample region corresponding to a first sub-region of a sample organ or tissue, and segment, from the target image, a first target region corresponding to a first sub-region of an organ or tissue, the sample organ or tissue being of a same type as the organ or tissue of the target object; a morphological characteristic determination module configured to determine a morphological characteristic value of the first sample region in the each sample image, and determine a morphological characteristic value of the first target region in the target image; and/or an analysis module configured to determine a condition of the organ or tissue of the target object, based on age of each of one or more sample objects in at least one portion of the plurality of sample images, a morphological characteristic value of a first sample region in each one of the at least one portion of the plurality of sample images, the age of the target object, and the morphological characteristic value of the first target region.

In some embodiments, the determination of a condition of the organ or tissue of the target object may include: determining a first ranking of the morphological characteristic value of the first target region among the morphological characteristic values of the first sample regions in a first portion of the plurality of sample images, based on the age of the each of the one or more sample objects in the at least one portion of the plurality of sample images, the morphological characteristic value of the first sample region in the each one of the at least one portion of the plurality of sample images, the age of the target object, and the morphological characteristic value of the first target region; and/or determining, based on the first ranking, the condition of the organ or tissue of the target object. The sample objects in the first portion of the plurality of sample images and the target object may be of a substantially same or similar age when the target image and the first portion of the plurality of sample images are acquired, respectively.

In some embodiments, the determination of a condition of the organ or tissue of the target object may include: determining at least one percentile value of morphological characteristic values of first sample regions in sample images corresponding to each age, by determining, based on age of the sample object in the each sample image when the each sample image is acquired, and the morphological characteristic value of the first sample region in the each sample image, a second ranking of the morphological characteristic values of the first sample regions in the sample images corresponding to the each age; determining, based on the at least one percentile value, a third ranking of the morphological characteristic value of the first target region in the target image among the morphological characteristic values of the first sample regions in a second portion of the plurality of sample images; and/or determining, based on the third ranking, the condition of the organ or tissue of the target object. The sample objects in the second portion of the plurality of sample images and the target object may be of a substantially same or similar age when the target image and the second portion of the plurality of sample images are acquired, respectively.

In some embodiments, the determination of a condition of the organ or tissue of the target object may include: fitting out a curve representing a relation of the morphological characteristic values of the first sample regions in the plurality of sample images and age of the sample objects in the plurality of sample images, by using age as an independent variable, and using at least one percentile value of morphological characteristic values of first sample regions in sample images corresponding to each age as a dependent variable; and/or determining a percentile value of the morphological characteristic value of the first target region in the target image among the morphological characteristic values of the first sample regions in a third portion of the plurality of sample images, by comparing the age of the target object and the morphological characteristic value of the first target region in the target image with the fitted curve. The sample objects in the third portion of the plurality of sample images and the target object may be of a substantially same or similar age when the target image and the third portion of the plurality of sample images are acquired, respectively.

In some embodiments, the sample data acquisition module may be further configured to acquire gender of the sample object in the each sample image. The target data acquisition module may be further configured to acquire gender of the target object. The analysis module may be further configured to determine a fourth ranking of the morphological characteristic value of the first target region in the target image among the morphological characteristic values of the first sample regions in a fourth portion of the plurality of sample images, and/or determine, based on the fourth ranking, the condition of the organ or tissue of the target object. The sample objects in the fourth portion of the plurality of sample images and the target object may be of a substantially same or similar age when the target image and the fourth portion of the plurality of sample images are acquired, respectively.

In some embodiments, the target data acquisition module may be further configured to obtain at least two target images of the target object, the at least two target images being corresponding to different ages when the at least two target images are acquired, respectively. The image segmentation module may be further configured to segment, from each of the at least two target images, a first target region corresponding to the first sub-region of the organ or tissue. The morphological characteristic value determination module may be further configured to determine a morphological characteristic value of the first target region in the each of the at least two target images. The analysis module may be further configured to determine a target variation trend of the morphological characteristic values of the first target regions corresponding to the target object, based on the morphological characteristic values of the first target regions in the at least two target images corresponding to different ages, determine a reference variation trend of a morphological characteristic value of a first sample region corresponding to the first sub-region of the organ or tissue, based on age corresponding to each of a fifth portion of the plurality of sample images, and the morphological characteristic value of the first sample region in the each of the fifth portion of the plurality of sample images, and/or determine the condition of the organ or tissue in the target image of the target object by comparing the target variation trend and the reference variation trend.

In some embodiments, the image segmentation module may be further configured to segment, from the each sample image, a second sample region corresponding to a second sub-region of the organ or tissue, and/or segment, from the target image, a second target region corresponding to the second sub-region of the organ or tissue. The morphological characteristic determination module may be further configured to determine a morphological characteristic value of the second sample region in the each sample image, and/or determine a morphological characteristic value of the second target region in the target image. The analysis module may be further configured to determine the condition of the organ or tissue of the target object, based on age of the sample object in the each sample image when the each sample image is acquired, the morphological characteristic value of the second sample region in the each sample image, the age of the target object, and the morphological characteristic value of the second target region in the target image.

In some embodiments, the image segmentation module may be further configured to segment, using a deep learning model and/or a template matching algorithm, the first sample region from the each sample image, and/or the first target region from the target image.

