CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Japanese Patent Application No. 2019-184673, filed Oct. 7, 2019. The contents of that application are incorporated by reference herein in their entirety.

TECHNICAL FIELD

The present disclosure relates to a segmentation device.

BACKGROUND ART

Technology of performing segmentation on an image or the like obtained by an X-ray CT scan (for example, see Japanese Unexamined Patent Publication No. H8-215192) is known.

BRIEF SUMMARY

In the related art, segmentation of a biological tissue in a medical image has been mathematically performed on the basis of CT values, concentration values, or the like. In this case, there is a problem in that it is difficult to segment tissues with close CT values, concentration values, or the like. A person's intervention (determination) is required for segmentation in consideration of an influence of conditions at the time of imaging or variables such as individual differences. Accordingly, there is demand for improvement in segmentation accuracy without requiring a person's intervention.

An objective of the present disclosure is to provide a segmentation device that can improve segmentation accuracy.

According to an aspect of the present disclosure, there is provided a segmentation device including: an input unit configured to receive an input of data of a maxillofacial region or a constituent maxillofacial region which is a partial region of a maxillofacial part; a calculation unit configured to perform segmentation of a biologically important region using the data of the constituent maxillofacial region input to the input unit and a previously generated learning model, and to calculate a three-dimensional position of the biologically important region in the constituent maxillofacial region; and an output unit configured to output information based on a result of calculation from the calculation unit. The learning model is a learning model which is generated using training data such that segmentation data of the biologically important region is output when the data of the constituent maxillofacial region is input. The data of the constituent maxillofacial region is image data which is acquired by an X-ray CT scan or an MRI scan of the constituent maxillofacial region. The biologically important region is at least one region of blood vessels, neural tubes, and a mandibular canal passing through the constituent maxillofacial region and a biological tissue passing through the mandibular canal.

With this segmentation device, segmentation of a biologically important region is performed using a constituent maxillofacial region and a previously generated learning model. The learning model is a learning model which is generated using training data such that segmentation data of the biologically important region is output when the data of the constituent maxillofacial region is input. The data of the constituent maxillofacial region is image data which is acquired by an X-ray CT scan or an MRI scan of the constituent maxillofacial region. Accordingly, it is possible to segment a biologically important region from image data acquired by an X-ray CT scanner or an MRI scanner. By performing segmentation using the learning model in this way, a likelihood of improvement in segmentation accuracy increases, for example, in comparison with a case in which segmentation is mathematically performed on the basis of a CT value, a concentration value, or the like. With improvement in accuracy, a likelihood of a person's intervention not being required also increases.

The learning model may be a learning model which is generated using the training data such that segmentation data of a region of interest in a biologically normal region which is a region outside of the biologically important region in the constituent maxillofacial region is additionally output when the data of the constituent maxillofacial region is input, and the calculation unit may be configured to perform segmentation of the region of interest. Accordingly, it is possible to segment the region of interest from the constituent maxillofacial region.

The calculation unit may be configured to calculate three-dimensional positional relationship information between the biologically important region and the region of interest. Accordingly, it is possible to understand a three-dimensional positional relationship between the biologically important region and the region of interest.

The region of interest may be at least one region of a tooth region, a region which is occupied by an artifact implanted in the tooth region, a boundary region between a jawbone and the tooth region, a boundary region between the jawbone and the artifact, and an alveolar region, and the learning model may be a learning model which is generated using the training data such that segmentation data of each region of interest is output when the data of the constituent maxillofacial region is input. Accordingly, it is possible to segment each region of interest from the constituent maxillofacial region.

The learning model may include: a first learning model which is generated using first training data such that segmentation data of a tooth region is output when the data of the constituent maxillofacial region is input; and a second learning model which is generated using second training data such that segmentation data of the biologically important region is output when the data of the constituent maxillofacial region and the segmentation data of the tooth region are input. The calculation unit may be configured to acquire the segmentation data of the tooth region using the data of the constituent maxillofacial region input to the input unit and the first learning model, and to perform segmentation of the biologically important region using the acquired segmentation data of the tooth region, the data of the constituent maxillofacial region input to the input unit, and the second learning model. By using the first learning model and the second learning model in combination in this order, the likelihood of improvement in segmentation accuracy increases further in comparison with a case in which the learning models are independently used. Particularly, since segmentation is performed with a focus on the tooth region, it is possible to further improve segmentation accuracy more than when segmentation is performed along with other regions.

The second learning model may be a learning model which is generated using the second training data such that segmentation data of the biologically important region is output when at least one of a panoramic tomographic image along a curve of a dental arch and a cross-section image crossing the curve is input, and the calculation unit may be configured to set a spline curve for the segmentation data of the tooth region acquired using the first learning model, to generate at least one of a panoramic tomographic image along the set spline curve and a cross-section image crossing the set spline curve, and to perform segmentation of the biologically important region using the generated image and the second learning model. Accordingly, it is possible to segment a biologically important region in a panoramic tomographic image, a cross-section image, or the like. A biologically important region (blood vessels, neural tubes, a mandibular canal, a biological tissue passing through the mandibular canal) is visibly presented in images such as a tomographic image and a cross-section image.

The calculation unit may be configured to calculate a distance between the region of interest and the biologically important region as an inter-element distance. Accordingly, it is possible to calculate a specific distance between a region of interest and a biologically important region.

The output unit may be configured to display information of the inter-element distance calculated by the calculation unit. Accordingly, it is possible to acquire information of an inter-element distance.

The learning model may include a third learning model which is generated using third training data such that the inter-element distance is output when the segmentation data of the tooth region and the segmentation data of the biologically important region are input, and the calculation unit may be configured to calculate the inter-element distance using the segmentation data of the tooth region, the segmentation data of the biologically important region, and the third learning model. By calculating the inter-element distance using the third learning model in this way, it is possible to increase a likelihood of improvement in calculation accuracy of the inter-element distance.

The calculation unit may be configured to calculate a difficulty level of an implant treatment or a difficulty level of tooth extraction in accordance with the inter-element distance. Accordingly, it is possible to understand a difficulty level of an implant treatment or a difficulty level of tooth extraction.

Information of an implant may be additionally input to the input unit, and the calculation unit may be configured to calculate a distance between the implant and the biologically important region when the implant is implanted on the basis of the information of the implant input to the input unit. The distance calculated in this way can be used as a matter for determination of an implant treatment.

The output unit may be configured to issue an alarm when a position of the implant when the implant is implanted is close to the biologically important region or overlaps the biologically important region. Accordingly, it is possible to notify of a risk of an implant treatment.

The output unit may be configured to present information of an implant which is usable on the basis of a result of calculation from the calculation unit. Accordingly, it is possible to propose an implant which is suitable for an implant treatment.

The calculation unit may be configured to additionally generate an image of a biologically normal region, and the output unit may be configured to present a synthetic image of the image of the biologically normal region generated by the calculation unit and an image of the segmentation data of the biologically important region. Accordingly, an image in which both a biologically normal region and a biologically important region are presented can be used as a matter for determination of an implant treatment.

The input unit may be configured to receive an input of a user's operation of designating a treatment target position, the output unit may be configured to present an indicator of the treatment target position in an image of the constituent maxillofacial region according to the designated treatment target position, and the calculation unit may be configured to calculate a positional relationship between the biologically important region and the indicator of the treatment target position. Accordingly, a positional relationship between a treatment target position designated by a user and a biologically important region can be used as a matter for determination of an implant treatment.

The input unit may be configured to receive an operation of moving the indicator. Accordingly, it is possible to easily change an indicator of a treatment target position.

The indicator of the treatment target position may be an indicator of an implant. Accordingly, it is possible to calculate a positional relationship between a biologically important region and an implant.

The calculation unit may be configured to calculate a distance between the indicator of the treatment target position and the biologically important region as the inter-element distance. Accordingly, it is possible to calculate a specific distance between a treatment target position and a biologically important region.

The output unit may be configured to issue an alarm when the indicator of the treatment target position is close to the biologically important region by less than the inter-element distance or overlaps the biologically important region. Accordingly, it is possible to notify of a risk of an implant treatment.

The output unit may be configured to present information of an implant which is usable on the basis of the result of calculation from the calculation unit. Accordingly, it is possible to propose an implant which is suitable for an implant treatment.

According to the present disclosure, it is possible to provide a segmentation device that can improve segmentation accuracy.

