JP7039665B2 - Information processing equipment, operation method and program of information processing equipment - Google Patents

Description

The present invention relates to a technique for generating image data related to a predetermined part of the eye portion.

With the advent of a device called Optical Coherence Tomogoraphy (hereinafter referred to as OCT), it is possible to obtain volume image data consisting of multiple two-dimensional tomographic images (hereinafter referred to as tomographic images) of the retina of the eye to be inspected. It has become possible. In the medical field of ophthalmology, the user interprets the layered structure based on the volume image data and observes the state of the lesion.

In order to observe the state of the lesion in the volume image data of the retina, it is effective to display a tomographic image, analyze the image of the layer structure of the retina, and display a layer thickness graph or the like at the same time. By grasping the position of the tomographic image on the fundus image (designated position information) and the position of a predetermined layer on the fundus image (layer position information), the user can detail both the state of the surface of the fundus and the state of the deep tissue. It is disclosed in Patent Document 1 that it can be observed and grasped.

Japanese Unexamined Patent Publication No. 2008-73099

Here, it is effective for the user to pay attention to a specific layer of the retina in order to easily grasp the details of the state of the lesion in the eye portion. At this time, the user not only confirms the display of the layer position information for a specific layer of the retina (for example, the specific layer of the retina is displayed in a different display color on the tomographic image), but also volume rendering and the layer thickness graph. , Layer thickness map, etc. need to be displayed respectively. However, if each display is performed independently, the operation is complicated.

Therefore, one of the objects of the present invention is to generate image data related to the selected layer in the eye portion.

One of the information processing apparatus of the present invention is the analysis image data obtained by analyzing each of a plurality of tomographic images of the eye portion taken at different dates and times by an optical interference tomometer for ophthalmology, and was selected. An information processing device that displays the analyzed image data related to the layer of the eye on the display in chronological order , and shows the classification information of the layer structure obtained by the layer structure analysis of the tomographic image of the eye. The projected images relating to the layers of the eye portion selected in response to the operator's instruction while the images are displayed on the display unit are arranged in the time series as the analysis image data and displayed on the display unit, and the eyes are displayed. The layer thickness data in the first analysis region selected in the unit is acquired for each of the plurality of tomographic images taken at different dates and times, and is different from the first analysis region in the direction intersecting the depth direction. In the second analysis region, the layer thickness data in the second analysis region selected in the eye portion is acquired for each of the plurality of tomographic images taken at the different dates and times, and in the depth direction. The positional relationship between the first analysis region and the second analysis region in the intersecting direction is displayed on the display unit in an identifiable state, and the layer thickness data in the first analysis region are arranged in chronological order. The first time-series data and the second time-series data in which the layer thickness data in the second analysis region are arranged in time series are shown on a common vertical axis corresponding to the layer thickness and common to date and time. It is characterized in that a control means for displaying on the display unit in an identifiable state is provided on the layer thickness graph having the horizontal axis of.
Further, one of the present inventions includes an operation method of the above-mentioned information processing apparatus and a program for causing a computer to execute the operation method.

According to one of the present inventions, it is possible to generate image data related to a selected layer in the eye.

It is a figure which shows the system configuration including the fundus image display apparatus which concerns on embodiment of this invention. It is a figure which shows the structure of the fundus image display apparatus which concerns on embodiment of this invention. It is a flowchart which shows the processing of the fundus image display apparatus which concerns on embodiment of this invention. It is a figure which shows typically the B-scan image in the xz direction. It is a figure which shows the example of the interface which is displayed when the user selects the display layer of interest. It is a figure which shows the display example of the analysis image data of the tomographic image which clarified the layer structure classification. It is a flowchart which shows the generation process of the analysis image data of the tomographic image which clarified the layer structure classification. It is a figure which shows the display example of the analysis image data which shows the layer thickness graph about the selected display layer. It is a flowchart which shows the generation process of the analysis image data which shows the layer thickness graph about the selected display layer. It is a figure which shows the display example of the analysis image data which shows the layer thickness map about the selected display layer. It is a flowchart which shows the generation process of the analysis image data which shows the layer thickness map about the selected display layer. It is a figure which shows the display example of the analysis image data which shows the volume rendering of a selected display layer. It is a flowchart which shows the generation process of the analysis image data which shows the volume rendering of a selected display layer. It is a figure which shows the display example of the analysis image data which shows the projection image about the selected display layer. It is a flowchart which shows the generation process of the analysis image data which shows the projection image about the selected display layer. It is a figure which shows the other example of the interface which is displayed when a user selects a display layer. It is a figure which shows the layer structure of a retina schematically. It is a figure which shows another example of the selection method of a display layer. It is a figure which shows the display example of the fundus image display apparatus. In the glaucoma diagnosis, it is a figure for demonstrating the case where GCC is selected as a display layer of interest by a user, and the display of a plurality of analysis image data is linked based on the selection of a display layer, so that diagnosis becomes easy. It is a flowchart which shows the processing by the processing part at the time of glaucoma diagnosis. It is a figure which shows the display example of the fundus image display apparatus which performs the follow-up observation. It is a figure which shows the graph example which shows 2 layer thicknesses of the region | region which has a large layer thickness change by comparing the data of the past 3 times corresponding to 2204 of FIG.

Hereinafter, preferred embodiments to which the present invention is applied will be described in detail with reference to the accompanying drawings. However, the following is a description of an example of a preferred embodiment of the present invention, and the present invention is not limited thereto.

First, the first embodiment of the present invention will be described. In the present embodiment, the volume image data (three-dimensional tomographic image data) of a predetermined part of the eye portion, here the retina of the eye to be inspected, and the layer structure classification result (segmentation) obtained by the layer structure analysis of the volume image data. It is possible to display the analysis image data related to the layer selected from the divided layer structure by using and. Examples of the analysis image data include analysis image data showing a two-dimensional tomographic image (hereinafter referred to as a tomographic image) that clearly shows the layer structure classification, analysis image data showing a layer thickness graph for the selected layer, and a selected layer. There are analytical image data showing a layer thickness map of the selected layer, analytical image data showing the volume rendering of the selected layer, analytical image data showing the projected image of the selected layer, and the like.

FIG. 1 is a diagram showing a system configuration including a fundus image display device 1 according to the present embodiment. As shown in FIG. 1, the fundus image display device 1 is connected to the tomographic image imaging device 2 and the data server 3 via a local area network (LAN) 4 such as Ethernet (registered trademark). The tomographic image imaging device 2 is a device that images a tomographic image of the eye portion, and OCT is used in this embodiment. Since the OCT acquires a plurality of tomographic images in one imaging, the volume image data of the retina can be acquired by arranging these tomographic images in order. The tomographic image imaging device 2 captures a tomographic image of the eye of a subject (patient) according to an operation by a user (engineer or doctor), and outputs volume image data acquired from the tomographic image to the fundus image display device 1. do. As another embodiment, the fundus image display device 1 may be connected to a data server 3 that stores the volume image data obtained by the tomographic image imaging device 2, and may be configured to acquire necessary volume image data from the data server 3. good. The connection with these devices may be configured to be connected via an interface such as USB or IEEE1394, or may be configured to be connected via an external network such as the Internet by LAN4. The fundus image display device 1 is a configuration that serves as an application example of an image processing device and an ophthalmic system.

FIG. 2 is a diagram showing a configuration of a fundus image display device 1 according to the present embodiment. The fundus image display device 1 is composed of an image acquisition unit 11, an image analysis unit 12, a display layer selection unit 13, an analysis image generation unit 14, a display unit 15, and a processing unit 16.

Next, FIG. 19 is a diagram showing a display example of the fundus image display device 1 according to the present embodiment. FIG. 19 is an example of the display unit 15 of the fundus image display device 1, and has a display layer selection unit 13 that can be operated by the user in the display. 1901 is a display example of the analysis image data of the tomographic image in which the layer structure classification regarding the selected display layer is clarified. Reference numeral 1902 is a display example of analysis image data showing a layer thickness graph for the selected display layer. Reference numeral 1903 is a display example of analysis image data showing a layer thickness map for the selected display layer. Reference numeral 1904 is a display example of analysis image data showing volume rendering of the selected display layer. Reference numeral 1905 is a display example of analysis image data showing a projection image of the selected display layer.

Next, the process of the fundus image display device 1 according to the present embodiment will be described with reference to FIGS. 2 and 3. FIG. 3 is a flowchart showing the processing of the fundus image display device 1 according to the present embodiment.

In step S301, the image acquisition unit 11 acquires volume image data from the tomographic image imaging device 2 and the data server 3 and outputs the volume image data to the image analysis unit 12.

In step S302, the image analysis unit 12 extracts the division information of the layer structure of NFL, IPL, OPL, INL, ONL, and IS / OS-RPE from the B-scan image of the volume image data.

(Display of tomographic image)
FIG. 4 is a diagram schematically showing a B-scan image (tomographic image) in the xz direction. The volume image data is configured by superimposing a B-scan image in the y direction with respect to a B-scan image in the xz direction. The NFL, IPL, OPL, INL, ONL, and IS / OS-RPE in FIG. 4 are medically in the order of nerve fiber layer, inner plexiform layer, outer plexiform layer, inner nuclear layer, outer nuclear layer, and intracellular photoreceptor. It is called the outer plexiform junction-retinal pigment epithelium. In step S302, the image analysis unit 12 extracts layer structure classification information for all B-scan images. The image analysis unit 12 outputs the volume image data acquired in step S301 and the layer structure division information extracted in step S302 to the analysis image generation unit 14.

The user selects a layer of interest (hereinafter referred to as a display layer) so that the acquired volume image data of the retina can be easily observed. In response to this, in step S303, the display layer selection unit 13 outputs information indicating the display layer selected by the user (hereinafter referred to as display layer information) to the analysis image generation unit 14.

In step S304, the analysis image generation unit 14 generates analysis image data using the volume image data, the layer structure division information, and the display layer information. That is, the analysis image generation unit 14 generates analysis image data related to the selected display layer and outputs the analysis image data to the display unit 15.

In step S305, the display unit 15 displays the analysis image data related to the display layer. As a result, the user can browse the analyzed image data, and it becomes easy to observe the lesion of the retina. The display control process executed when the display unit 15 displays the analysis image data is a processing example of the display control means.

As described above, when the user selects the display layer of interest, the analysis image data related to the selected display layer can be browsed. Further, when a plurality of analysis image data are generated, it is possible to switch the display or update the display among the plurality of analysis image data. That is, it is possible to switch the display of a plurality of analysis image data (in conjunction with each other) based on the selected layer by the switching unit (not shown).

Next, the analysis image data will be specifically described by taking glaucoma diagnosis as an example. FIG. 5 is a diagram showing an example of an interface displayed when the user selects a display layer of interest. In FIG. 5, NFL501 is a selection item for the nerve fiber layer, GCC502 is medically a selection item for the ganglion cell complex (NFL and IPL), and Retina 503 is a selection item for the entire retinal layer. In glaucoma diagnosis, the diagnosis is facilitated by paying attention to the state of GCC. Therefore, it is assumed that the user has performed an operation of selecting the GCC 502 as the display layer using the interface shown in FIG. As a result, in step S303, the display layer information indicating GCC is output from the display layer selection unit 13.

In the following step S304, the analysis image generation unit 14 selects the analysis image data showing the tomographic image clearly indicating the layer structure classification by using the volume image data, the layer structure classification information, and the display layer information indicating the GCC. Analytical image data showing the layer thickness graph for the selected display layer, analytical image data showing the layer thickness map for the selected display layer, analytical image data showing volume rendering for the selected display layer, projection image for the selected display layer The analysis image data showing the above is generated at the same time.

Here, the analysis image data showing the tomographic image in which the layer structure classification is clearly described will be specifically described with reference to FIGS. 6 and 7. FIG. 6 is a diagram showing a display example of analysis image data of a tomographic image in which the layer structure classification is clearly shown. FIG. 7 is a flowchart showing a process of generating analysis image data of a tomographic image in which the layer structure classification is clearly shown.

In step S701, the analysis image generation unit 14 acquires volume image data, layer structure division information, and display layer information. In step S702, the analysis image generation unit 14 generates a tomographic image from the volume image data, and generates analysis image data including a line drawing display 601 that visualizes the division information of the layer structure. In step S703, the analysis image generation unit 14 performs image processing for highlighting the display layer indicated by the display layer information on the analysis image data generated in step S702. That is, the tomographic image of the OCT is a shading image, and the analysis image generation unit 14 performs coloring 602 (dot representation in FIG. 6) for emphasizing the NFL and IPL image regions corresponding to the GCC which is the display layer. ..

(Layer thickness graph)
Next, the analysis image data showing the layer thickness graph for the selected display layer will be specifically described with reference to FIGS. 8 and 9. FIG. 8 is a diagram showing a display example of analysis image data showing a layer thickness graph for the selected display layer. FIG. 9 is a flowchart showing a process of generating analysis image data showing a layer thickness graph for the selected display layer.

GCC layer thickness measurement is important for the diagnosis of glaucoma. Usually, glaucoma is diagnosed by measuring the intraocular pressure because glaucoma occurs frequently when the intraocular pressure is higher than normal. In addition, if glaucoma is suspected by fundus examination, a visual field test is performed. Each test takes time, and the measured values are not stable due to factors such as fatigue as the test time elapses. However, GCC layer thickness measurement by OCT can diagnose changes in layer thickness that frequently occur in glaucoma from the initial stage, and since stable measurement is possible, it not only assists conventional diagnosis but also glaucoma. In some cases, more accurate diagnosis may be possible at the initial stage of. In step S901, the analysis image generation unit 14 acquires volume image data, layer structure division information, and display layer information. In step S902, the analysis image generation unit 14 measures the thickness of the NFL and IPL regions corresponding to GCC in all the B-scan images constituting the volume image data. The thickness measured here is a change between the layer thickness in the z-axis direction and the layer thickness in the x-axis direction of the colored region 602 in FIG. In step S903, the analysis image generation unit 14 generates analysis image data obtained by drawing a layer thickness graph representing the measured thickness as a graph, as shown in FIG. The analysis image data generated here is the analysis image data of the layer thickness graph for the selected display layer.

(Layer thickness map)
Next, the analysis image data of the layer thickness map for the selected display layer will be specifically described with reference to FIGS. 10 and 11. FIG. 10 is a diagram showing a display example of analysis image data showing a layer thickness map for the selected display layer. FIG. 11 is a flowchart showing a process of generating analysis image data showing a layer thickness map for the selected display layer.

The layer thickness distribution of GCC is important for the diagnosis of glaucoma. Usually, glaucoma is diagnosed by performing a visual field test and finding an abnormality in visual acuity that is biased in the visual field. However, the visual field test takes time, and the measured value is not stable due to factors such as announcement as the test time elapses. In addition, it is difficult to diagnose glaucoma by visual field test in the initial symptoms where the abnormal visual acuity biased in the visual field is mild. However, since GCC layer thickness distribution measurement by OCT enables stable measurement, it not only assists conventional diagnosis, but also finds abnormal layer thickness distribution in the initial stage, which makes it more effective in the early stage of glaucoma. Highly accurate diagnosis may be possible. In step S1101, the analysis image generation unit 14 acquires volume image data, layer structure division information, and display layer information. In step 1102, the analysis image generation unit 14 measures the thickness of the NFL and IPL regions corresponding to GCC in all the B-scan images constituting the volume image data. The thickness measured here is a change between the layer thickness in the z-axis direction and the layer thickness in the x-axis direction of the colored region 602 in FIG. 6, and the distribution is measured over the entire B-scan image in the y-axis direction. In step S1103, as shown in FIG. 10, the analysis image generation unit 14 generates analysis image data including a layer thickness map in which the distribution in the xy direction is drawn for the layer thickness change. The analysis image data generated here is the analysis image data of the layer thickness map for the selected display layer. Further, as shown in FIG. 10, the nine numbers displayed in the layer thickness map divide the retina into nine regions, and the average value of the GCC layer thickness in each region is calculated and displayed by image analysis. is doing.

(Volume rendering)
Next, the analysis image data showing the volume rendering of the selected display layer will be specifically described with reference to FIGS. 12 and 13. FIG. 12 is a diagram showing a display example of analysis image data showing volume rendering of the selected display layer. FIG. 13 is a flowchart showing a process of generating analysis image data indicating volume rendering of the selected display layer.

For the diagnosis of glaucoma, it is important to display the GCC in 3D and grasp the points of interest in three dimensions and the surrounding situation. By displaying GCC three-dimensionally, it is possible to distinguish whether the thinned part is the original organic one or the thinned part due to the etiology by comparing the shape with the surrounding situation. In step S1301, the analysis image generation unit 14 acquires volume image data, layer structure division information, and display layer information. In step S1302, as shown in FIG. 12, the analysis image generation unit 14 performs volume rendering on the pixel data in the NFL and IPL regions corresponding to GCC in all the B-scan images constituting the volume image data and analyzes them. Generate image data. The analysis image data generated here is the analysis image data of the volume rendering of the selected display layer.

(Projection image)
Next, the analysis image data of the projection image relating to the selected display layer will be specifically described with reference to FIGS. 14 and 15. FIG. 14 is a diagram showing a display example of analysis image data showing a projection image of the selected display layer. FIG. 15 is a flowchart showing a process of generating analysis image data showing a projection image of the selected display layer.

Displaying a projected image of GCC is important for diagnosing glaucoma. The projected image can confirm the blood vessel running state in GCC. If there is abnormal blood vessel running, other causes are suspected, not glaucoma. In step S1501, the analysis image generation unit 14 acquires volume image data, layer structure division information, and display layer information. Here, the analysis image generation unit 14 uses the maximum value projection method as the projection method. That is, in step S1502, the analysis image generation unit 14 projects the maximum value on the xy plane of the pixel data in the NFL and IPL regions corresponding to GCC in all the B-scan images constituting the volume image data to produce the projected image. Analyzing image data is generated by drawing. The analysis image data generated here is the analysis image data of the projection image relating to the selected display layer.

(Switches the display of multiple analysis image data in conjunction with each other based on the layer selection)
In glaucoma diagnosis, FIGS. 19 and 20 show cases in which GCC is selected as the display layer of interest to the user and the display of a plurality of analysis image data is linked and switched based on the selection of the display layer to facilitate the diagnosis. Will be explained using. In step S2001, when performing glaucoma diagnosis, the user performs an operation of selecting the display layer selection unit 13, that is, specifically, GCC 502 as the display layer using the interface shown in FIG. Subsequently, in step 2002, analysis image data showing a tomographic image clearly indicating GCC, analysis image data showing a layer thickness graph related to GCC, analysis image data showing a layer thickness map related to GCC, and analysis image data showing volume rendering of GCC, Analyzed image data showing a projected image related to GCC is generated, and the display is updated for each.

By observing the tomographic image related to GCC when GCC is selected, it is confirmed that the shape of the tomographic image forms a gentle layer distribution. If there is an abnormal shape change in the shape of the tomographic image, it will be easy to distinguish it from other causes.

By observing the analysis image data showing the layer thickness graph related to GCC when GCC is selected, there is no abnormality in the shape of the tomographic image mentioned above that suggests other etiologies, and the layer change graph has a gentle distribution. It is easily presumed that glaucoma is caused by the fact that it is thinner than normal.

By displaying the analysis image data showing the layer thickness map related to GCC, even if there is a tomographic image or thinning that cannot be observed on the tomographic surface, the entire fundus can be easily confirmed and a part of the visual field is affected. The thinning of a part of a GCC layer can be easily found. In that case, an accurate diagnosis can be made by observing the fault plane at the thinned part again.
By observing the analysis image data showing the volume rendering related to GCC when GCC is selected, the shape of the GCC layer can be grasped three-dimensionally. It is possible to easily distinguish whether the thinning of GCC is due to the original organic or the etiology of glaucoma, which assists in the diagnosis of glaucoma.

By observing the analysis image data showing the projected image related to GCC when GCC is selected, it is possible to easily confirm the presence or absence of abnormalities in blood vessel running in the thinned part found in the layer thickness map of GCC. It is easy to distinguish whether the thinning is due to the etiology of glaucoma, which assists in the diagnosis of glaucoma.

As described above, by linking a plurality of analysis display data based on the selection of the display layer, the diagnosis of glaucoma becomes easy.

(Judgment flow regarding diagnosis of glaucoma)
The processing by the processing unit 16 at the time of diagnosing glaucoma will be described with reference to FIG. In glaucoma diagnosis, the user (doctor) first confirms whether there are any organic abnormalities other than GCC layer thinning by a tomographic image that clearly shows GCC passing through the center of the macula. For example, if the patient has symptoms of decreased visual acuity or narrowing of the visual field and there is no abnormality other than the GCC layer thickness in the tomographic image passing through the center of the macula, the possibility of glaucoma is high and it is helpful for diagnosis. Therefore, in step S2101, the processing unit 16 causes the user to input whether or not there is an abnormality other than the GCC layer thickness in the tomographic image passing through the center of the tomographic spot (diagnosis result input UI in the tomographic image (user interface). )) Is displayed on the display unit 105. In step S2102, the processing unit 16 determines whether or not it is input in the UI that there is no abnormality other than the GCC layer thickness in the tomographic image passing through the center of the macula. If it is input that there is no abnormality other than the GCC layer thickness, the process proceeds to step S2103. On the other hand, in other cases, the process proceeds to step S2111.

Next, the user pays attention to the analysis image data showing the GCC layer thickness graph in the tomographic image passing through the center of the macula. If the graph of layer thickness in the tomographic image passing through the center of the macula is all or partly thinner than the standard thickness, it is an important basis for diagnosing glaucoma. Therefore, in step S2103, the processing unit 16 causes the user to input whether or not the graph of the layer thickness in the tomographic image passing through the center of the macula is completely or partially thinner than the standard thickness (UI (layer)). The diagnosis result input UI in the thickness graph) is displayed on the display unit 105. In step S2104, the processing unit 16 determines whether or not it is input that the graph of the layer thickness is completely or partially thinner than the standard thickness in the UI. If it is entered that the layer thickness graph is all or partially thinner than the standard thickness, the process proceeds to step S2105. On the other hand, in other cases, the process proceeds to step S2111.

The user then focuses on the analytical image data showing the layer thickness map for the GCC of the retina. In the layer thickness map for GCC around the center of the macula, normally, the thickness is thick on the optic disc side and thin on the macula side in the left-right direction, and the thickness at the same layer thickness is symmetrically distributed in the vertical direction. In glaucoma, the possibility of glaucoma is very high if, for example, a thin part in the lower part of the layer thickness map distribution occurs frequently, or if the layer thickness map distribution is uneven as a whole. Therefore, in step S2105, the processing unit 16 causes the user to input whether or not, for example, a thin portion on the lower side is frequently generated in the layer thickness map distribution, or the layer thickness map distribution is uneven as a whole. The UI (diagnosis result input UI in the layer thickness map) is displayed on the display unit 105. In step S2106, the processing unit 16 determines whether or not it is input that the layer thickness map distribution is non-uniform in the UI. If it is input that the layer thickness map distribution is non-uniform, the process proceeds to step S2107. On the other hand, in other cases, the process proceeds to step S2111.

Next, the user looks at the analysis image data showing the volume rendering of GCC, observes the GCC layer three-dimensionally, and makes a definitive diagnosis of glaucoma based on whether or not there is deformation of the retinal shape, that is, organic abnormality. If there is deformation, it becomes possible to suspect other causes. Therefore, in step S2107, the processing unit 16 causes the display unit 105 to display a UI (diagnosis result input UI in the volume rendered image) for causing the user to input whether or not the retina shape is deformed. In step S2108, the processing unit 16 determines whether or not it is input that there is no deformation of the retinal shape in the UI. If it is input that there is no deformation of the retinal shape, the process proceeds to step S2112. On the other hand, in other cases, the process proceeds to step S2109.

Next, the user looks at the analysis image data showing the projected image of GCC, and observes the situation of blood vessel scanning that is abundant in the GCC layer. By observing the blood vessels, it is possible to know whether there are any abnormalities due to vascular occlusion or poor blood circulation, or whether new blood vessels are generated. If there are no particular abnormalities in the blood vessel scanning status, it is useful for the definitive diagnosis of glaucoma. Abnormal blood vessel scanning conditions can lead to suspicion of other etiologies. Therefore, in step S2109, the processing unit 16 displays a UI (diagnosis result input UI in a projected image) for allowing the user to input whether or not there is an abnormality due to vascular occlusion or poor blood circulation or the occurrence of new blood vessels. To display. In step S2110, the processing unit 16 determines whether or not it is input in the UI that there is no abnormality due to vascular occlusion or poor blood circulation or no new blood vessel is generated. When it is input that there is no abnormality due to vascular occlusion or poor blood circulation or no new blood vessel is generated, the process proceeds to step S2112. On the other hand, in other cases, the process proceeds to step S2111. In step S2111, the processing unit 16 causes the display unit 105 to indicate that there is a suspicion of another etiology. In step S2112, the processing unit 16 displays the definitive diagnosis of glaucoma on the display unit 105. As a result, the user can grasp the diagnosis result regarding glaucoma.

As shown in this embodiment, in the glaucoma diagnosis, by selecting GCC as the display layer and referring to a plurality of analyzed image data at the same time, the glaucoma diagnosis is made from the layer thickness graph and the analyzed image data of the layer thickness map. Improve diagnostic accuracy by eliminating the possibility of other etiologies by referring to volume rendering and projected image analysis image data as well as making a quick definitive diagnosis from a complex diagnosis. It will be possible to defeat it.

As described above, when the user selects GCC as the display layer when diagnosing glaucoma, the analysis image data related to GCC, which is the selected display layer, that is, the analysis image data showing the tomographic image clearly indicating GCC, the layer related to GCC. It is possible to generate analysis image data showing a thickness graph, analysis image data showing a layer thickness map related to GCC, analysis image data showing volume rendering of GCC, and analysis image data showing a projection image related to GCC. Further, one of these analysis image data may be selected, or a plurality of analysis image data may be selected to be generated and displayed. Further, analysis image data other than these may be generated and displayed.

Further, in the present embodiment, the selection of the display layer is set manually by the user. For example, when a patient suspected of having glaucoma is imaged at the time of examination, disease data indicating glaucoma is stored in the volume image data. Then, when displaying the analysis image data related to glaucoma, the fundus image display device 1 may automatically select GCC as the display layer and display the analysis image data related to GCC. This makes it possible to easily grasp the details of the pathological condition of the eye.

(Second embodiment: follow-up)
Next, a second embodiment of the present invention will be described. Follow-up is important in diagnosing glaucoma. For example, whether or not the condition of glaucoma has progressed compared to the past can be accurately diagnosed not only by subjective data such as visual acuity and visual field, but also by GCC layer thickness data and the progress of the layer thickness map.

FIG. 22 is a diagram showing a display example of the fundus image display device 1 for performing follow-up observation according to the present embodiment. It has a display layer selection unit 13 that can be operated by the user. The form of this display device is to compare and display the analysis image data for the past three times of shooting. Reference numeral 2201 is a display example of the analysis image data of the tomographic image in which the layer structure classification regarding the selected display layer is clearly shown. Reference numeral 2202 is a display example of analysis image data showing a layer thickness graph for the selected display layer. Reference numeral 2203 is a display example of analysis image data showing a projection image of the selected display layer. 2204 compares the data corresponding to a plurality of time points (here, the past three times) in the GCC layer thickness map, and as a result of the comparison, graphs the two layer thicknesses of the region where the layer thickness changes significantly. It is the one displayed.

FIG. 23 is a diagram showing an example of a graph corresponding to 2204 in FIG. 22 showing two layer thicknesses in a region where the data of the past three times are compared and the layer thickness changes significantly. The changes in layer thickness in the regions 6 and 7 are illustrated graphically.

The flow follow-up of glaucoma diagnosis will be described with reference to FIG. In the glaucoma diagnosis, first, attention is paid to the analysis image data showing the GCC layer thickness graph in the tomographic image passing through the central part of the macula of 2201. By comparing whether the graph of the layer thickness in the tomographic image passing through the center of the macula is thinned with the progress by the comparison part (not shown), the degree to which the course of glaucoma affects the visual acuity and the progress of the medical condition Can be grasped.

Next, we focus on the progress of the analysis image data showing the layer thickness map of 2202 retina GCC. If partial thinning is observed in the 9-divided area rather than overall thinning, it is possible to understand the possibility of tunnel vision as the glaucoma progresses, even if there are few subjective symptoms. ..

Next, in 2203, it is possible to observe the change in the situation of blood vessel scanning, which is abundant in the GCC layer, by looking at the analysis image data showing the projected image of GCC. Follow-up observations can be used to determine whether visual acuity abnormalities are caused by anything other than glaucoma, depending on whether vascular abnormalities due to age-related macular degeneration are observed in elderly patients or whether there is a change in vascular scanning status in diabetic patients. can.

Next, in 2204, in the GCC layer thickness map shown in 2202, the progress of glaucoma is progressed by comparing the data of the past three times and observing two layer thicknesses in the region where the layer thickness changes significantly. It is possible to grasp the degree in a short time with a progress graph. As shown in this embodiment, in the follow-up observation of glaucoma, not only subjective data such as visual acuity and visual field, but also GCC layer thickness data, layer thickness map, projection image, and progress graph of layer thickness map are grasped at the same time. This makes it possible to carry out an accurate examination.

(Third embodiment: selection of layer when diagnosing diseases related to blood vessels)
Next, a third embodiment of the present invention will be described. Diseases of the retina include bleeding from blood vessels, stenosis of blood vessels, and obstruction. As for blood vessels, running of blood vessels is often seen in IPL as shown in 1701 of FIG. Therefore, selecting IPL as the display layer of interest is useful for diagnosing diseases related to blood vessels.

FIG. 16 is a diagram showing another example of the interface displayed when the user selects a display layer. In the interface shown in FIG. 16, it is possible to select whether or not to display the NFL, IPL, OPL, INL, ONL, and IS / OS-RPE layers for each layer.

As shown in 1601 of FIG. 16, the user selects the IPL as the display layer by checking the check box 1601 of the IPL. Then, as described in the first embodiment, steps S304 and S305 generate and display the analysis image data related to the display layer. That is, attention is paid to the running of blood vessels on the IPL by displaying the analysis image data showing the tomographic image clearly indicating the IPL, the analysis image data showing the projection image related to the IPL, the analysis image data showing the volume rendering of the IPL, and the like. Will be easier. Further, by displaying the analysis image data showing the layer thickness graph related to the IPL and the analysis image data showing the layer thickness map related to the IPL, it is possible to confirm whether or not there is an abnormality such as bleeding caused by a blood vessel. In this way, by selectively displaying the analysis image data related to the IPL which is the display layer, it is possible to easily make a diagnosis regarding blood vessel running.

(Fourth embodiment: selection of layer when diagnosing other diseases)
Next, a fourth embodiment of the present invention will be described. In the present embodiment, as a method of selecting a display layer, the analysis image data is used as an interface for selecting the display layer. That is, in the present embodiment, for example, the analysis image data showing the tomographic image clearly indicating the layer structure classification as shown in FIG. 6 is used as an interface for selecting the display layer.

In the present embodiment, it is possible to switch the display / non-display of the analysis image data related to each layer by clicking the mouse on each layer in the state where the analysis image data showing the tomographic image clearly indicating the layer structure classification is displayed. .. Each time the user clicks, the display layer can be switched between colored and uncolored. 1801 in FIG. 18 is a pointer that is moved and displayed by operating the mouse. In a state where the NFL and IPL coloring display is set, the user can display the IS / OS-RPE in color by moving the pointer 1801 to the position of the IS / OS-RPE and clicking the mouse. Following this, in steps S304 and S305 described in the first embodiment, analysis image data related to the selected display layer is generated and displayed. As a result, the user can easily select the display layer to be focused on, and the display of the analysis image data is switched by switching the selection of the display layer. In this embodiment, the selection of the display layer in the tomographic image in which the layer structure classification is clearly shown is exemplified, but the display layer may be selected by using the display of other analysis image data.

In addition, there is vitiligo in the disease of the retina. An example of vitiligo is shown in 1702 of FIG. In this way, vitiligo occurs frequently in INL and OPL. Therefore, when diagnosing vitiligo, a display layer focusing on INL and OPL is selected. As a result, analysis image data related to INL and OPL are generated and displayed, and the diagnosis of vitiligo becomes easy.

In addition, age-related macular degeneration is a disease of the retina. For the diagnosis of age-related macular degeneration, it is useful to observe the presence or absence of choroidal neovascularization in RPE and the presence or absence of edema, hemorrhage, and cyst in the peripheral layer. An example of a cyst is shown in 1703 of FIG. The cysts are most common in ONL and OPL. Therefore, in order to diagnose age-related macular degeneration, RPE or a display layer focusing on RPE and peripheral layers (ONL, OPL, etc.) is selected. As a result, RPE or RPE and analysis image data of the peripheral layer are generated and displayed, and the diagnosis of age-related macular degeneration becomes easy.

In the above embodiment, the selection of the display layer is set manually, but the layer suspected to be abnormal is automatically selected as the display layer by using the layer structure classification result by the layer structure analysis of the volume image data. You may.

Further, in the above embodiment, the image analysis unit 12 extracts a total of 6 layers of NFL, IPL, OPL, INL, ONL, and IS / OS-RPE from the volume image data as shown in FIG. However, the embodiment of the present invention is not limited to the extraction of 6 layers. For example, further extraction of ILM (internal limiting membrane), GCL (ganglion cell layer), OPL (outer plexiform layer), ELM (outer plexiform membrane), IS / OS (visual cell internal limiting membrane), RPE (retinal). Even when there are 6 or more layers such as the pigment epithelial layer), the same treatment can be performed.

(Other embodiments)
Further, each of the above-described embodiments exemplifies a case where the present invention is applied to a fundus image display device. However, the application example of the present invention is not limited to the fundus image display device. The present invention can be implemented as, for example, a system composed of a plurality of devices, a device composed of one device, software or a program operating on a computer, a storage medium such as an optical disk, or the like.

The present invention is also realized by executing the following processing. That is, software (program) that realizes the functions of the above-described embodiment is supplied to the system or device via a network or various storage media, and the computer (or CPU, MPU, etc.) of the system or device reads the program. This is the process to be executed.

1: Fundus image display device, 11: Image acquisition unit, 12: Image analysis unit, 13: Display layer selection unit, 14: Analysis image generation unit, 15: Display unit

Claims (11)

Analytical image data obtained by analyzing each of a plurality of tomographic images of the eye portion taken at different dates and times by an ophthalmic optical interference tomometer, and the analytical image data relating to the selected layer of the eye portion. It is an information processing device that displays on the display unit in chronological order.
The layer of the eye part selected according to the instruction of the operator in the state where the tomographic image showing the division information of the layer structure obtained by the layer structure analysis of the tomographic image of the eye part is displayed on the display part. The projected images of the above are arranged in the time series as the analysis image data and displayed on the display unit, and the layer thickness data in the first analysis region selected in the eye unit is captured by the plurality of faults taken at different dates and times. The layer thickness data in the second analysis region selected in the eye portion, which is the second analysis region acquired for each image and different from the first analysis region in the direction intersecting the depth direction. Is acquired for each of the plurality of tomographic images taken at different dates and times, and the positional relationship between the first analysis region and the second analysis region in the direction intersecting the depth direction can be discriminated. A second time-series data in which the layer thickness data in the first analysis area is arranged in time series and a second time-series data in which the layer thickness data in the second analysis area is arranged in time series are displayed on the display unit. The time series data is displayed on the display unit in an identifiable state on a layer thickness graph having a common vertical axis corresponding to the layer thickness and a common horizontal axis corresponding to the date and time. An information processing device characterized by this. The information processing apparatus according to claim 1, wherein the first analysis region and the second analysis region are regions selected from a plurality of different regions in a direction intersecting the depth direction. The control means causes the display unit to display each region of the plurality of different regions in a direction intersecting the depth direction in a state in which the display unit can identify the positional relationship. The information processing apparatus according to claim 2, wherein the information processing apparatus is displayed on the screen. Further provided with a comparison means for comparing layer thickness changes in the plurality of different regions in the time series.
The selected region is a region selected from the plurality of different regions based on the comparison result of the comparison means, assuming that the region has a larger layer thickness change than the other regions among the plurality of different regions. The information processing apparatus according to claim 2 or 3, wherein the information processing apparatus is characterized by the above. As the analysis image data, the control means further arranges a layer thickness map for the layer of the eye portion in time series and displays it in a first display area in the display unit, and displays the first time series data and the first time series data. Claims 1 to 4 characterized in that the time-series data of 2 is displayed on the layer thickness graph in a second display area different from the first display area in the display unit in an identifiable state. The information processing apparatus according to any one of the above items. The control means, as the analysis image data, is a second layer thickness graph different from the layer thickness graph, and the second layer thickness graph relating to the selected eye layer is displayed in the time series. The information processing apparatus according to any one of claims 1 to 5, wherein the information processing apparatus is displayed side by side on the display unit. As the second layer thickness graph, the control means displays the layer thickness graph in the tomographic image in which the layer structure classification regarding the selected eye layer is clearly arranged in the time series on the display unit. The information processing apparatus according to claim 6. The invention according to any one of claims 1 to 7, further comprising a selection means for selecting an arbitrary type of layer from a plurality of different types of layers as the layer of the eye portion based on an operator's instruction. The information processing device described. As the analysis image data, the control means further arranges the analysis image data of the tomographic image showing the division information of the layer structure in the time series and displays it on the display unit, and divides the projected image and the layer structure. The information processing apparatus according to any one of claims 1 to 8, wherein the analysis image data of the tomographic image showing the information is displayed side by side in the vertical direction on the display unit. Analytical image data obtained by analyzing each of a plurality of tomographic images of the eye portion taken at different dates and times by an ophthalmic optical interference tomometer, and the analytical image data relating to the selected layer of the eye portion. It is an operation method of an information processing device that displays on the display unit in chronological order.
The layer of the eye part selected according to the instruction of the operator in the state where the tomographic image showing the division information of the layer structure obtained by the layer structure analysis of the tomographic image of the eye part is displayed on the display part. The projected images of the above are arranged in the time series as the analysis image data and displayed on the display unit, and the layer thickness data in the first analysis region selected in the eye unit is captured by the plurality of faults taken at different dates and times. The layer thickness data in the second analysis region selected in the eye portion, which is the second analysis region acquired for each image and different from the first analysis region in the direction intersecting the depth direction. Is acquired for each of the plurality of tomographic images taken at different dates and times, and the positional relationship between the first analysis region and the second analysis region in the direction intersecting the depth direction can be discriminated. A second time-series data in which the layer thickness data in the first analysis area is arranged in time series and a second time-series data in which the layer thickness data in the second analysis area is arranged in time series are displayed on the display unit. A control step is provided for displaying the time-series data of the above on the display unit in an identifiable state on a layer thickness graph having a common vertical axis corresponding to the layer thickness and a common horizontal axis corresponding to the date and time. A method of operating an information processing device, which is characterized in that. A program that causes a computer to execute a step in the operation method of the information processing apparatus according to claim 10.

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