Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/0002—Inspection of images, e.g. flaw detection
  • G06T7/0012—Biomedical image inspection
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/10—Segmentation; Edge detection
  • G06T7/149—Segmentation; Edge detection involving deformable models, e.g. active contour models
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/10—Image acquisition modality
  • G06T2207/10072—Tomographic images
  • G06T2207/10081—Computed x-ray tomography [CT]
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/30—Subject of image; Context of image processing
  • G06T2207/30004—Biomedical image processing
  • G06T2207/30056—Liver; Hepatic

Definitions

• the present invention relates to a three-dimensional virtual liver surgery planning system, a three-dimensional virtual liver surgery planning system that provides doctors with an effective method for safe and reasonable surgery.
• Schindl et al. (2005. Schindl, MJ, Redhead, DN, Fearon, KCH, Garden, OJ, Wigmore, S J. The value of residual liver volume as a predictor of hepatic dysfunction and infection after major liver resection. 54 (2), 289-296.) Show that over 90% of patients with normal liver function have a greater than 90% incidence of severe liver dysfunction after surgery, whereas% RLV is less than 27%. Reported a 13% incidence of liver dysfunction.
• Ferrero et al. (2007. Ferrero, A., Vigano, L., Polastri, R., Muratore, A., Eminefendic, H., Regge, D .. Capussotti, L.
• Reasonable liver resection is required to be properly selected for the cutting position and orientation, and shape of the cross-cutting end face can be "plan
• the three-dimensional virtual liver surgery system is designed to replace existing livers, remnants, and grafts, as well as visual information about the location and size of tumors, the structure of liver-related blood vessels, and liver compartments. Quantitative information on the volume of the liver should be provided.
• the 3D virtual liver surgery planning system includes a digital imaging and communications in medicine (DICOM) receiving module that receives an abdominal computer tomography (CT) volume data set from a picture archiving and communication system (PACS) server. And a standard liver volume (SLV) from the DICOM loading and noise canceling models for loading and removing the received abdominal CT volume data set, and the multiple CT volume data set from which the noise is removed.
• DICOM digital imaging and communications in medicine
• a standard hepatic extrapolation module for estimating, hepatic extraction models for extracting a three-dimensional hepatic region connected with the standard hepatic extrapolation models, and a hepatic vein, hepatic artery, hepatic vein, and inferior vein vena cava, IVC) and the three-dimensional vessel region for extracting the vascular region, and the vascular extraction module, the three-dimensional tumor region, Tumor extraction modules for extracting the liver, liver segmentation module for dividing the extracted three-dimensional liver region into a plurality of compartments by a user-selected reference point or a sphere editing tool, and connected to the liver compartmentalization module, cutting plane, liver Block, or Includes a Liver Surgery Planning module that uses a spherical editing tool to perform a 3D liver plan.
• It may further include a procedure-based user-friendly interface connected to each of the above.
• the method may further include liver extraction correction models that interact with the liver extraction models and edit the extracted three-dimensional liver region.
• the blood vessel extraction module may further include a blood vessel extraction correction module capable of editing the extracted three-dimensional vessel region.
• the method may further include a tumor extraction correction module that interacts with the tumor extraction modules and edits the extracted three-dimensional tumor region.
• the liver extracting hairs may perform a semi-automatic hybrid liver extraction method.
• a step of deriving an initial liver region by applying a fast marching level set method using a plurality of seed points selected from a plurality of CT slices by a user Improving the derived first liver region through a threshold-based level set method, correcting the extracted liver region through a circle that can be scaled at a two-dimensional viewpoint, or at a three-dimensional viewpoint
• a correction step of correcting through an adjustable ball In the semi-automatic complex liver extraction method, a step of deriving an initial liver region by applying a fast marching level set method using a plurality of seed points selected from a plurality of CT slices by a user, Improving the derived first liver region through a threshold-based level set method, correcting the extracted liver region through a circle that can be scaled at a two-dimensional viewpoint, or at a three-dimensional viewpoint
• a correction step of correcting through an adjustable ball In the semi-automatic complex liver extraction method, a step of deriving an initial liver region by applying a fast marching level set method using a pluralit
• the 3D viewpoint selection may be implemented using an integrated 3D viewpoint restoration button in which buttons of front, rear, left side, right side, top side, and bottom side are integrated.
• the blood vessel extraction models may extract a three-dimensional region of the portal vein, hepatic artery, and hepatic vein by a region growing method using a threshold interval and seed points.
• the region growing method may include loading a CT volume in which the extracted liver region is cut, editing the liver region through a spherical editing tool to include the blood vessel to be extracted, and editing the liver.
• CT into area Masking the volume inputting a plurality of seed points selected by the user to the plurality of CT slices, and an initial threshold interval according to the intensity distribution of the data set of the masked CT volume finding an interval; generating a plurality of additional threshold intervals by finely adjusting a lower threshold and an upper threshold of the initial threshold interval; an initial threshold interval And extracting a plurality of blood vessel structures based on an additional threshold interval and providing the same with volume information in a three-dimensional view.
• the user may further include editing the extracted blood vessel through a circular or spherical editing tool at a 2D or 3D viewpoint.
• the liver compartmentalization step forming a segment 1, dividing the liver into the left and right lobe, dividing the right lobe into an anterior and a posterior sector, the left lobe Dividing into medial sector (segment 4) and lateral sector; dividing the posterior sector into segment 6 and segment 7; anterior sector) may be configured to perform a step of dividing all segments into segment 5 and segment 8 and dividing the lateral sector into segment 2 and segment 3. .
• the user can easily extract the liver, blood vessels, and tumors according to the procedure, divide the liver compartment, and plan the surgery through a user-centered interface. have.
• FIG. 1 is an overall configuration diagram of a three-dimensional virtual liver surgery planning system according to an embodiment of the present invention.
• FIG 2 is an information processing flowchart of the liver extraction step of the three-dimensional virtual liver surgery planning system according to an embodiment of the present invention.
• FIG. 3 is an image photograph showing an example of selecting seed points for liver extraction in a liver extraction step of a 3D virtual liver surgery planning system according to an embodiment of the present invention.
• FIG. 4 is an image showing an example of editing the region of the liver extracted in the liver extraction step of the three-dimensional virtual liver surgery planning system according to an embodiment of the present invention through a three-dimensional ball (size) that can be adjusted in size It is a photograph.
• Figure 5 is a flow chart of blood vessel extraction in the liver extraction step of the three-dimensional virtual liver surgery planning system according to an embodiment of the present invention.
• 6A to 6F illustrate six candidate blood vessel candidates extracted based on various threshold intervals to allow a user to select the best extraction result in a 3D virtual liver surgery planning system according to an embodiment of the present invention.
• 6A and 6H are graphs and tables showing the volume of candidate vessel structure candidates.
• FIG. 7 is a diagram exemplarily illustrating an integrated three-dimensional view restoration button having a color scheme in a three-dimensional virtual liver surgery planning system according to an embodiment of the present invention.
• FIG. 8 is a flowchart illustrating a three-step procedure based on a cutting plane for classifying liver compartments in a three-dimensional virtual liver surgery planning system according to an embodiment of the present invention.
• FIG. 9 is a flowchart illustrating a three-step procedure based on a spherical editing tool for classifying liver compartments in a three-dimensional virtual liver surgery planning system according to an embodiment of the present invention.
• FIG. 10 is a flowchart illustrating a liver compartment classification procedure in a 3D virtual liver surgery planning system according to an embodiment of the present invention.
• Image is a picture.
• FIG. 12 is an image photograph showing a liver divided into a right lobe and a left lobe through a spherical editing tool in a 3D virtual liver surgery planning system according to an exemplary embodiment of the present invention.
• FIG. 13A is an image photograph showing an example of a surgical plan through selecting a liver compartment to be cut in a 3D virtual liver surgery planning system according to an embodiment of the present invention
• FIG. 13B is a legend showing a volume and a color table of each compartment. .
• FIG. 14 is an image photograph showing an example of a surgical plan using a spherical editing tool in a three-dimensional virtual liver surgery planning system according to an embodiment of the present invention.
• 15A to 15F are image photographs showing an example of a procedure-driven user interface in a 3D virtual liver surgery planning system according to an embodiment of the present invention.
• FIG. 1 is an overall configuration diagram of a three-dimensional virtual liver surgery planning system according to an embodiment of the present invention.
• the three-dimensional virtual liver surgery planning system includes: DICOM (Digital Imaging and Communications in Medicine) receiving modules (Ml), DICOM loading and noise removing modules (M2), standard liver volume estimation modules (M3), liver extraction Module (M4), vascular extraction modules (M5), tumor extraction modules (M6), liver compartmentalization module (M7), liver surgery planning modules (M8), and liver extraction correction modules (M4) )
• DICOM Digital Imaging and Communications in Medicine
• M2 DICOM
• M2 standard liver volume estimation modules
• M4 liver extraction Module
• M5 vascular extraction modules
• tumor extraction modules M6
• liver compartmentalization module M7
• liver surgery planning modules M8
• liver extraction correction modules M4
• hepatic artery phase portal vein
• a computer tomography (CT) volume data set from phase and hepatic vein phase is input to the system.
• the DICOM receiving module Ml may receive CT volume data of various phases from a picture archiving and communication system (PACS) server and store it in a local storage.
• the DICOM loading and noise cancellation mode M2 loads the CT volume data set into the system to remove noise and registers the CT volume data set of each phase.
• liver volume (SLV) estimation models (M3) can provide standard liver volume (SLV) estimated through three regression models.
• Liver extraction models M4 use a noise-free portal volume CT volume data set to extract liver areas.
• the extracted liver can be visualized as a two-dimensional and three-dimensional view on the CT image, and the user can freely edit the three-dimensional liver region at two and three-dimensional points through the liver extraction correction module M41. .
• the liver extraction module M4 also provides the function of calculating the volume of the extracted liver.
• Blood vessel extraction module (M5) is a registered hepatic artery phase, portal vein phase, and hepatic vein image after noise is removed to extract four three-dimensional vessels (hepatic vein, hepatic artery, hepatic vein, and inferior vena cava) Use the CT volume data set of (phase) as input information.
• the extracted blood vessel may be visualized as a two-dimensional and three-dimensional view on the CT image, and the region of the extracted blood vessel may be effectively edited at the two-dimensional and three-dimensional views through the blood vessel extraction correction modules M51.
• the blood vessel extraction module M5 also provides the function of calculating the volume of the extracted blood vessel.
• the system of this example provides tumor extraction modules M6 to extract tumors from a CT volume data set.
• the extracted tumor can be visualized as a two-dimensional and three-dimensional view on the CT image, and the user can effectively edit the region of the extracted tumor in both the two-dimensional and three-dimensional views through the tumor extraction correction module (M61). Can be.
• the tumor extraction modules (M6) also provide the function of calculating the volume of the extracted tumor.
• the system of this embodiment classifies the extracted liver into several compartments.
• Liver compartmentalization modalities are provided.
• the liver segmentation module M7 provides an interactive method for the user to select a plurality of landmarks from a two-dimensional viewpoint on the CT image, wherein the selected reference points are the two-dimensional viewpoint and the three. It can be visualized in a dimensional view.
• the liver can be divided into sections based on cutting planes or spherical editing tools created by selected landmarks.
• the divided liver compartments can be visualized in a three-dimensional view, and a color scheme is provided to visualize the separated livers in different colors for each compartment.
• the hepatic compartmentalization models M7 provide an interactive way to adjust the color and transparency of each compartment and may also provide the ability to calculate the volume of each compartment. Liver compartmentalization may be corrected through liver compartmentalization correction models M71.
• the hepatic surgical planning model (M8) uses the results of the previous models, provides information on the relative spatial relationships within the liver, blood vessels, tumors, liver compartments and liver compartments, and visualizes them in a three-dimensional perspective. Provides the ability to The surgical planning model M8 provides one or more cutting planes for cutting out the liver portion of the tumor, and can adjust the direction, position and shape of the cutting plane, and utilize the cutting planes or spherical editing tools. Provides the ability to cut liver compartments. In addition, the liver surgery planning model M8 may visualize and provide relative liver volume information in a three-dimensional view for effective surgery planning. The user can utilize the liver surgery plan model M8 via the user interface 1 ; 1 to perform the virtual liver surgery plan.
• the liver extracting hairs may perform a semi-automatic hybrid liver extraction method. That is, the initial liver region is automatically extracted based on the selected seed point, and then the 3D liver region is manually extracted through additional correction. It demonstrates in detail below.
• a number of seed points can be placed in the area where the user (S42). Selected seed points may be indicated by red dots, for example. It is a good idea to select about five slices at the same interval from one CT volume data set. When the liver area is wide, it is necessary to select 7 to 15 seed points per slice, and when the liver area is narrow, 1 to 4 seed points per slice may be selected (see FIG. 3). Seed points can be selected to encompass the entire liver region, including the edges.
• the first liver region is derived through the fast marching level-set method (S43). If the derived first liver region is too wide to be extracted, the user may return to the seed point selection step and select seed points again.
• the initially formed liver region may be determined through a threshold-based level-set method (S45). If the extracted liver region is appropriate, the liver extraction operation may be terminated (S46). However, if the user wants to edit the extracted liver, the non-living part may be removed or the missing part may be restored through a circle or ball that can be adjusted in two-dimensional and three-dimensional views (S47). In other words, by providing a two-dimensional circle that can be adjusted or a three-dimensional ball that can be adjusted, the user can effectively remove an area other than the liver by clicking and dragging.
• Figure 4 is possible to adjust the size of the extracted liver in three-dimensional view
• the step of smoothing the surface of the corrected liver region may be performed, and the volume of the liver extracted in the liver extraction step may be calculated and displayed on the screen.
• Figure 5 is a flow chart of blood vessel extraction in the liver extraction step of the three-dimensional virtual liver surgery planning system according to an embodiment of the present invention.
• the user can use the spherical editing tool based on the extracted liver area.
• the region including the blood vessel is masked (S52).
• the user selects a plurality of seed points in the blood vessel region of the 1 to 2 CT slices using the mouse (S53).
• the vascular structure is extracted through a region growing method based on six threshold intervals (S55). Thereafter, the extracted six vessel structure candidates are shown in three dimensions with volume information (S56). By checking whether there are satisfactory results among the six vascular structure candidates (S57), and if there are satisfactory results among the six vascular structure candidates, the user may select the result as the final result (S58) (FIGS. 6A to 6). 6h). However, if there is no satisfactory result among the six candidates, the user may repeat the blood vessel extraction procedure again from the seed point selection step (S53).
• the user may correct the selected blood vessel structure at two and three dimensional views (S59). If the corrected result is satisfactory, the blood vessel extraction procedure is terminated. Otherwise, the correction procedure can be performed again.
• FIG. 7 illustrates an integrated three-dimensional viewpoint restoration button (A, P, S, I, L, and R) having a color scheme in a three-dimensional virtual liver surgery planning system according to an embodiment of the present invention. It is a figure shown normally. Two colors are available for the integrated buttons (light blue for the A, S, and L buttons, and yellow for the P, I, and R buttons). Each button has its own location, with the ⁇ button on the front, the ⁇ burrow on the back, the L burrow on the left, the R button on the right, the S button on the top, and the I button on the bottom. The user may select one of the integration buttons to restore the 3D viewpoint to a specific viewpoint.
• the integrated burner may be set to return to a specific direction at once when the direction or position is freely adjusted by the user on the 3D screen. For example, if you want to change the situation from the right side of the extracted 3D liver to the right side. Press to change the screen to view the liver from behind.
• the color combination system assigns light blue for the left, top, front, and yellow for the right, bottom, and back in consideration of the positional meaning of each viewpoint, which allows the user to easily recognize the meaning of the button. It is to be utilized.
• FIG. 8 is a flowchart illustrating a three-step procedure based on a cutting plane for classifying liver compartments in a three-dimensional virtual liver surgery planning system according to an embodiment of the present invention.
• the liver can be divided into several compartments based on the vascular structure of the hepatic vein and portal vein by the Couinaud model.
• the user can divide a compartment based on the vascular structure of the hepatic vein and portal vein by the Couinaud model.
• Reference marks may be selected using a computer mouse on the 2D CT image (S701).
• the selected place may be represented by a red dot to indicate that it is selected as a reference point.
• it is possible to indicate that the reference point is selected through the red sphere at the same position of the three-dimensional viewpoint.
• the user may correct the divided liver compartment by adjusting the cutting plane (S703).
• the position and angle of the cutting plane can be adjusted freely by dragging at the three-dimensional viewpoint.
• FIG. 9 is a flowchart illustrating a three-step procedure based on a spherical editing tool for classifying liver compartments in a three-dimensional virtual liver surgery planning system according to an embodiment of the present invention.
• the extracted liver region is loaded and covered on the original CT image (S711).
• liver compartments according to the Couinaud model are classified through the spherical editing tool (S712).
• the classified liver region may be corrected as necessary through the spherical editing tool (S713).
• FIG. 10 is a flow chart illustrating a liver compartment classification procedure in a 3D virtual liver surgery planning system according to an embodiment of the present invention
• FIG. 12 An image image showing three reference points (middle hepatic vein, entrance of right portal vein, gallbladder fossa) used to distinguish the left lobe
• FIG. 12 is an extracted three-dimensional liver region extracted based on a CT image. The image is divided into a right lobe and a left lobe through a segmentation sphere.
• segment 1 is formed (S721).
• the liver is a cutting plane or segmentation sphere (see FIG. 12) that passes through the middle hepatic vein, the entrance of right portal vein, and the gallbladder fossa (see FIG. 11). ) Are divided into right lobe and left lobe (S722).
• the right lobe can be divided into anterior sector and posterior sector along the postal vein (S723).
• the left lobe can be divided into medial sector (segment 4) and lateral sector along the left vein (S724).
• the posterior sector may be divided into segment 6 and segment 7 according to the right posterior portal structure (S725).
• the anterior sector can be divided into segment 5 and segment 8 according to the right portal vein structure (S726).
• the lateral sector may be divided into segment 2 and segment 3 according to the left portal vein structure (S727).
• the segments other than segment 1 in the liver segmentation procedure may be classified based on the structure of the portal vein and the hepatic vein by a cutting plane or a sphere editing tool generated based on a landmark.
• Liver compartmentalization may be in whole or in part depending on the needs of the user.
• FIG. 13A is an image photograph showing an example of a surgical plan by selecting a liver compartment to be cut in a 3D virtual liver surgery planning system according to an embodiment of the present invention
• FIG. 13B is a legend showing a volume and a color table of each compartment.
• liver compartments, blood vessels, and tumors were visualized using 3D rendering techniques. You can hide the compartment where the tumor is located Can be removed effectively. In other words, during the stage of liver surgery, the tumor is located
• Some of the three-dimensional liver compartments can be excised with one or more cutting planes defined by the user or with a spherical editing tool and removed by making the three-dimensional liver compartments with tumor fully transparent using checkboxes. It may be.
• the volume of the entire liver, the volume of the site to be resected, and the volume and ratio of the remaining liver after the ablation may be provided in real time on the 3D screen to assist the surgical planning.
• the hepatic section has a thickness of 2 mm, which is created by the sum of the cross section thickness of 1 mm and the cross section thickness of 1 mm.
• a certain area between three dimensions is required, and a three-dimensional object having a thickness is generated to effectively visualize the cut surface.
• it can be taken to have a thickness of 2mm in total by taking 1mm in and out of the cutting surface.
• FIG. 14 is an image photograph showing an example of a surgical plan using a spherical editing tool in a three-dimensional virtual liver surgery planning system according to an embodiment of the present invention.
• the spherical editing tool allows the user to effectively remove the liver area where the tumor is located.
• the cut plane of the cut liver is seen at the three-dimensional viewpoint.
• volume information such as total liver volume and residual liver volume ratio (% 1 ⁇ ) may be provided on a three-dimensional screen in real time to assist the liver compartment surgery planning.
• 15A to 15F are image photographs showing an example of a procedure-oriented user interface in a 3D virtual liver surgery planning system according to an embodiment of the present invention.
• the user-oriented interface allows the user to easily extract liver, blood vessels and tumors according to the procedure, divide the liver compartments, and plan the surgery.

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• 2013
  • 2013-03-04 WO PCT/KR2013/001717 patent/WO2013129895A1/fr not_active Ceased

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