BACKGROUND

Field

The present disclosure relates to an apparatus and a method for amplifying the contrast of a contrast-enhanced CT (CE CT) image based on deep learning.

Description of the Related Art

When computed tomographic scanning (CT) is used, a subject enters a large circular machine with an X-ray generator to be scanned so that a cross-sectional image intersecting a human body may be obtained. It has an advantage in that structures are less overlaid than in simple X-ray imaging so that structures and lesions can be more clearly observed. Therefore, CT is being widely utilized for a complete check-up for most organs and diseases.

The contrast of CT images can be said to be very important to precisely diagnose lesions, and therefore efforts to acquire a high contrast image from a CT image are continuing together with the development of the CT examination methods. Generally, the lesion has a characteristic in that vascularity is higher or lower than the other surrounding tissues. Therefore, when a CT scanning is performed after injecting a contrast medium which increases an X-ray attenuation degree of a blood vessel, the image contrast between the lesion and the surrounding tissue becomes clearer. In clinical practice, contrast enhancement CT exam (with a contrast medium injection) is utilized as an important exam method for acquiring an image with an improved contrast of a lesion.

However, most of the efforts of the related art for amplifying the contrast of the CT image require an injection of a large amount of contrast media, which potentially causes patient hazard. Specifically, when the recent concern on the risk of the contrast media is considered, the efforts for acquiring a high quality diagnostic image need to be accompanied by efforts for minimizing the amount of contrast media injection.

As an example of such efforts, CT (computed tomography) manufacturers are releasing CT systems including a dual energy scanning function. The dual energy CT may improve the image contrast for contrast media component so that it is possible to reduce an injection amount of contrast media. However, the dual energy CT system of the related art is more expensive than the existing products, and as such it is difficult to be generally supplied. In addition, the degree of improvement of image contrast by the dual energy is limited, and therefore it cannot be a true solution and a new solution is required. That is, it is required to develop a more effective technique to acquire a high-quality diagnostic image (a high contrast CT image) while minimizing the amount of contrast media injection.

In the meantime, the deep learning technology is a new type of computing technique which achieves a specific purpose using a general-purpose program code which can be trained according to a given learning data set, instead of coding a series of detailed programs and its excellent performance is recognized in various image processing fields.

However, in order to show the desirable performance of the deep learning model, it is not only required to ensure enough training data sets, but also it needs to be accompanied by a method of partitioning data to allow the deep learning model to be trained for data in a previously designated range and to be operated for data in the previously designated range even in an actual usage stage. In order to apply the deep learning to medical images in which safety is particularly important, it can be said that the development of the effective deep learning training technology which satisfies such prerequisites is even more important.

A background art of the present disclosure is disclosed in Korean Unexamined Patent Application Publication No. 10-2020-0026071.

SUMMARY

The present disclosure has been made an effort to solve the problems of the related art and the object thereof is to provide an apparatus and a method for amplifying the image contrast of a CE CT image based on deep learning which are capable of achieving a high-quality diagnostic image (CT image) while minimizing the amount of contrast medium injection.

The present disclosure has been made an effort to solve the problems of the related art and the object thereof is to provide a training (learning) method of a deep learning model by means of an apparatus for amplifying the image contrast of a CE CT image based on deep learning to more effectively apply the deep learning to medical images in which the safety is specifically important.

The present disclosure has been made an effort to solve the problems of the related art and the object thereof is to provide an apparatus and a method for amplifying the image contrast of a CE CT image based on deep learning and a training method of a deep learning model therethrough which ensure the performance and the safety of the deep learning while utilizing the advantages of the deep learning.

However, technical objects to be achieved by the embodiment of the present disclosure are not limited to the technical objects as described above and other technical objects may be present.

As a technical means to achieve the above-described technical object, according to an aspect of the present disclosure, a deep learning based CE CT image contrast amplifying method includes: extracting at least one component CT image either a contrast enhancement (CE) component or a non-contrast enhancement (non-CE) component from an input CE CT image with the input CE CT image as an input to a previously trained deep learning model; and outputting a contrast-amplified CT image with respect to the input CE CT image based on the input CE CT image and the said at least one extracted component CT image.

Further, according to an aspect of the present disclosure, a deep learning based CE CT image contrast amplifying method may further include before the said extracting of at least one component CT image: extracting exam information from an input CE CT image; and selecting at least one deep learning model corresponding to the exam information among a plurality of previously trained deep learning models, and the extracting of at least one component CT image may include extracting at least one component CT image with respect to the input CE CT image with the input CE CT image as an input to the said at least one selected deep learning model.

In the deep learning based CE CT image contrast amplifying method according to one aspect of the present disclosure, the outputting of a contrast-amplified CT image may include: multiplying the input CE CT image and at least one component CT image by each predetermined ratio, respectively, followed by summing of the multiplied results.

In the deep learning based CE CT image contrast amplifying method according to one aspect of the present disclosure, the outputting of a contrast-amplified CT image may include: generating a first CT image and a second CT image by multiplying the input CE CT image and at least one component CT image by each predetermined ratio, respectively, followed by summing of the multiplied results; and outputting a composite color image by applying a predetermined color tone table to the first and the second CT images.

Further, according to one aspect of the present disclosure, a deep learning based CE CT image contrast amplifying method may further include before the extracting of at least one component CT image: composing at least one component CT image dataset based on the image pairs of a first training CT dataset; generating a second training CT dataset based on the said at least one composed component CT image dataset and the first training CT dataset; and generating and training a deep learning model to be trained, in advance, using the second training CT dataset to extract at least one component CT image, and the said previously trained deep learning model may be a deep learning model which is trained by the training.

Further, the deep learning based CE CT image contrast amplifying method according to one aspect of the present disclosure may further include before the extracting of exam information: composing at least one component CT image dataset based on the image pairs of a first training CT dataset; generating a second training CT dataset consisting of image pairs each based on the said at least one composed component CT image and an original image of the said first training CT dataset; extracting exam information from the second training CT dataset and grouping the second training CT dataset into a plurality of groups according to a predetermined rule; and generating and training of a plurality of deep learning models to be trained such that each deep learning model to be trained may correspond to each group of the second training CT dataset, and the said plurality of previously trained deep learning models considered in the said selecting may be the plurality of deep learning models to be trained which is trained by the said generating and training.

In the meantime, according to another aspect of the present disclosure, a deep learning based CE CT image contrast amplifying apparatus may include: an image extracting unit which extracts at least one component CT image with respect to an input CE CT image with the input CE CT image as an input to a previously trained deep learning model; and an amplified image output unit which outputs a contrast-amplified CT image based on at least one extracted component CT image and the input CE CT image.

Further, the deep learning based contrast-enhanced image contrast amplifying apparatus may further include: an exam information extracting unit which extracts exam information from an input CE CT image; and a deep learning model selecting unit which selects at least one deep learning model corresponding to the exam information among a plurality of previously trained deep learning models, and the said image extracting unit may input the input CE CT image to at least one deep learning model selected by the exam information extracting unit and the deep learning model selecting unit to extract at least one component CT image with respect to the input CE CT image.

In the deep learning based CE CT image contrast amplifying apparatus, according to an aspect of the present disclosure, the image output unit may multiply the input CE CT image and at least one component CT image by each predetermined ratio, respectively, followed by summing of the multiplied results to produce output.

In the deep learning based CE CT image contrast amplifying apparatus, according to an aspect of the present disclosure, the amplified image output unit may multiply the input CE CT image and at least one component CT image by each predetermined ratio, respectively, followed by summing of the multiplied results to generate a first CT image and a second CT image, respectively, and apply a predetermined color tone table to the said first and second CT images to output a composed color image.

Further, the deep learning based CE CT image contrast amplifying apparatus may further include: a deep learning training unit which generates and trains a plurality of deep learning models to be trained, the deep learning training unit composes at least one component CT image dataset based on image pairs of a first training CT dataset; generates a second training CT dataset consisting of image pairs each based on the said at least one composed component CT image and an original image of the said first training CT dataset; and generates and trains a deep learning model to be trained, in advance, using the second training CT dataset to extract at least one component CT image, and the previously trained deep learning model may be a deep learning model which is trained by the training.

Further, the deep learning based CE CT image contrast amplifying apparatus may further include: a deep learning group training unit which generates and trains a plurality of deep learning models to be trained, wherein the deep learning group training unit composes at least one component image dataset based on image pairs of a first training CT dataset; generates a second training CT dataset consisting of image pairs each based on at least one composed component CT image and an original image of the first training CT dataset; and extracts exam information from the second training CT dataset and groups the second training CT dataset into a plurality of groups according to a predetermined rule, and generates and trains a plurality of deep learning models to be trained such that each deep learning model to be trained may correspond to each group of the second training CT dataset, and the plurality of previously trained deep learning models may be the plurality of deep learning models to be trained which is trained by the deep learning group training unit.

The above-described solving means are merely illustrative but should not be construed as limiting the present disclosure. In addition to the above-described embodiments, additional embodiments may be further provided in the drawings and the detailed description of the present disclosure.

According to the above-described solving means of the present disclosure, the contrast of the CT image is amplified based on deep learning to acquire a high-quality diagnostic image (CT image) while minimizing the amount of a contrast medium injection.

According to the above-described solving means of the present disclosure, the plurality of deep learning models (a plurality of deep learning models to be trained) is trained according to the exam information of the CT image to effectively apply the deep learning to a medical image in which the safety is specifically important.

According to the above-described solving means of the present disclosure, an apparatus and a method for amplifying a contrast of a CE CT image based on deep learning, and a deep learning model training (learning) method therethrough which ensure the performance of the deep learning and secure the safety while utilizing the advantages of the deep learning may be provided.

However, the effect which can be achieved by the present disclosure is not limited to the above-described effects and there may be other effects.

