Abstract:
A scanner ( 18 ) acquires images of a subject. A 3D model ( 52 ) of an organ is selected from an organ model database ( 50 ) and dropped over an image of an actual organ. A best fitting means ( 62 ) globally scales, translates and/or rotates the model ( 52 ) to best fit the actual organ represented by the image. A user uses a mouse ( 38 ) to use a set of manual tools ( 68 ) to segment and manipulate the model ( 52 )1:o match the image data. The set of tools ( 68 ) includes: a Gaussian tool ( 72 ) for deforming a surface portion of the model along a Gaussian curve, a spherical push tool ( 80 ) for deforming the surface portion along a spherical surface segment, and a pencil tool ( 90 ) for manually drawing a line to which the surface portion is redefined.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
   This application claims the benefit of U.S. provisional application Ser. No. 60/512,453 filed Oct. 17, 2003, and provisional application Ser. No. 60/530,488 filed Dec. 18, 2003, which are both incorporated herein by reference. 

   DESCRIPTION 
   The present invention relates to the diagnostic imaging systems and methods. It finds particular application in conjunction with the model based image segmentation of diagnostic medical images and will be described with particular reference thereto. Although described by the way of example with reference to x-ray computer tomography, it will further be appreciated that the invention is equally applicable to other diagnostic imaging techniques which generate 3D image representations. 
   Radiation therapy has been recently experiencing a transition from conformal methods to Intensity Modulation Radiation Therapy (IMRT). IMRT enables an improved dose distribution in the patient&#39;s body and makes possible precise delivery of high radiation dose directly to the tumor while maximally sparing the surrounding healthy tissue. Accurate target and “organ at risk” delineation is important in IMRT. Presently, the procedure is performed manually in 2D slices, which is cumbersome and the most time-consuming part of the radiation therapy planning process. The use of robust and reliable automatic segmentation technique would substantially facilitate the planning process and increase patient throughput. 
   Model based image segmentation is a process of segmenting (contouring) medical diagnostic images that is used to improve robustness of segmentation methods. Typically, a pre-determined 3D model of the region of interest or organ to be segmented in the diagnostic image is selected. The model represents an anatomical organ such as a bladder or femur, but it may also represent a structure such as a target volume for radiotherapy. In many cases, the model can be used to aid automated image segmentation by providing knowledge of the organ shape as an initial starting point for the automated segmentation process. However, in some instances, the auto-segmentation of the image may not be possible, or it is not robust enough to fit a specific organ or a section of the model accurately. Particularly, application of the auto-segmentation to the image data is difficult due to insufficient soft tissue contrast in CT data, high organ variability, and image artifacts, e.g. caused by dental fillings or metal implants. It would be desirable to be able to initiate the segmentation with a model and further complete an accurate segmentation when auto-segmentation is not practical or to enhance the auto-segmentation result for specific situations after auto-segmentation has been completed. 
   There is a need for the method and apparatus to provide the image segmentation of the model based image that is easily adapted to match a specific patient&#39;s anatomy. The present invention provides a new and improved imaging apparatus and method which overcomes the above-referenced problems and others. 
   In accordance with one aspect of the present invention, a diagnostic imaging system is disclosed. A means selects a shape model of an organ. A means best fits the selected model to an image data. A manual means modifies selected regions of the model to precisely match the image data. 
   In accordance with another aspect of the present invention, a method of segmenting an image of a diagnostic imaging system is disclosed. A shape model of an organ is selected. The selected model is dragged and dropped on an image data. The selected model is globally scaled, rotated and translated to best fit the image data. Local regions of the model are modified with a set of manual tools to precisely match the image data. 
   One advantage of the present invention resides in enabling the manipulation of the models to match subject&#39;s anatomy. 
   Another advantage resides in providing a set of diagnostic image modification tools enable the user to modify the models with a mouse. 
   Still further advantages and benefits of the present invention will become apparent to those of ordinary skill in the art upon reading and understanding the following detailed description of the preferred embodiments. 

   
     The invention may take form in various components and arrangements of components, and in various steps and arrangements of steps. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. 
       FIG. 1  is a diagrammatic illustration of a diagnostic imaging system; 
       FIG. 2  is a graphical representation of an organ model using triangular surfaces for use with aspects of the present invention; 
       FIGS. 3-4  are graphical representations of a Gaussian pull tool in accordance with the present invention; 
       FIGS. 5-6  are graphical representations of a Sphere push tool in accordance with the present invention; 
       FIGS. 7-9  are graphical representations of a Pencil tool in accordance with the present invention. 
   

   With reference to  FIG. 1 , an operation of an imaging system  10  is controlled from an operator workstation  12  which includes a hardware means  14  and a software means  16  for carrying out the necessary image processing functions and operations. Typically, the imaging system  10  includes a diagnostic imager such as CT scanner  18  including a non-rotating gantry  20 . An x-ray tube  22  is mounted to a rotating gantry  24 . A bore  26  defines an examination region of the CT scanner  18 . An array of radiation detectors  28  is disposed on the rotating gantry  24  to receive radiation from the x-ray tube  22  after the x-rays transverse the examination region  26 . Alternatively, the array of detectors  28  may be positioned on the non-rotating gantry  20 . 
   Typically, the imaging technician performs a scan using the workstation  12 . Diagnostic data from the scanner  18  is reconstructed by a reconstruction processor  30  into 3D electronic image representations which are stored in a diagnostic image memory  32 . The reconstruction processor  30  may be incorporated into the workstation  12 , the scanner  18 , or may be a shared resource among a plurality of scanners and workstations. The diagnostic image memory  32  preferably stores a three-dimensional image representation of an examined region of the subject. A video processor  34  converts selected portions of the three-dimensional image representation into appropriate format for display on a video monitor  36 . The operator provides input to the workstation  12  by using an operator input device  38 , such as a mouse, touch screen, touch pad, keyboard, or other device. 
   With continuing reference to  FIG. 1  and further reference to  FIG. 2 , an organ model database  50  stores predetermined models  52  of specific organs and general shapes of areas of interest that could correspond to radiotherapy treatment areas of interest, by e.g., shapes approximating a tumor shape to be treated with radiation. Typically, the organ models  52  are defined as a set of polygons describing a surface. Preferably, the polygons are triangles and represented by a flexible triangular mesh  54 . The basic structure is a list of vertices in (x, y, z) coordinates and a list of polygons which identify the vertices which comprise each polygon, e.g. each triangle has three vertices. Storage of basic structures and automatic segmentation of images using such triangular structures is more fully described in pending U.S. patent application Ser. No. 10/091,049 having Publication No. 2002/0184470 A-1 entitled Image Segmentation by Weese, et al. 
   With continuing reference to  FIG. 1 , the user selects a model of an organ from the organ model database  50  via a model selecting means  60 . Preferably, the software  16  includes a user interface means which allow the user to select models by dragging and dropping the organ model over the subject anatomy represented by the image data while watching a superimposition of the diagnostic image and the organ model on the monitor  36 . Various displays are contemplated. In one advantageous display format, three orthogonal slices through the 3D image which intersect at a common point are displayed concurrently in quadrants of the monitor. By shifting the intersection point and/or dragging the organ model, the fit in all three dimensions is readily checked. 
   The user best fits the model to the organ using of a set of global tools  62  which apply transformation to the entire model on the image. The global tools  62  include rotation, translation and scaling tools that allow the user to rotate, translate, and scale the model. The global tools  62  are applied by a use of the mouse  38  on each (x, y, z) dimension of the model, e.g. the mouse motion is converted into translation, scale or rotation such that all vertices in the model are transformed by the defined translation, scale, or rotation. 
   An auto-segmentation means or process  64  automatically adapts the best fitted model to the boundaries of the anatomical structures of interest. By sliding the intersection point, the user can check the fit in various directions and slices. If the user determines that the results of the auto-segmentation process  64  are not satisfactory, e.g. the desired segmentation accuracy is not achieved, the user initiates image modification via an image modification means  66  which includes a set of manual local tools  68  which allows the user to manipulate local regions of the model  52  to match the image data more accurately or in accordance with user&#39;s preferences. Alternatively, when the user determines that the auto-segmentation is not possible, the auto-segmentation process  64  is skipped. The local tools  68  comprise three main functions: selection of the local region (vertices) to be modified, the method by which the vertices are transformed, and the translation of the mouse motion into parameters defining the transformation. 
   The selection of the vertices is based either on the distance from the mouse position or the layers of vertex neighbors from the closest vertex to the mouse location. In the first case, all vertices within a specified distance from the mouse are selected. In the latter case, the vertex closest to the mouse is selected. All vertices which share a triangle with the first vertex are considered neighbors and comprise the first neighbor layer. A second neighbor layer is all vertices which share a triangle with any of the first layer of vertices. In this case, the selection of the vertices to be deformed is based on the number of neighbor layers to be used. 
   Additionally, control parameters related to local manipulation tools are stored as part of the organ model. In this way, optimal tool settings are maintained as part of the organ model. Of course, it is also contemplated that the manual tools ( 68 ) may be used to manipulate boundaries between multiple organs at one time or within a regional area with a single mouse motion. 
   The image undergoing segmentation and segmented images are stored in a data memory  70 . 
   With continuing reference to  FIG. 1  and further reference to  FIGS. 3-4 , a Gaussian pull tool  72  deforms the organ model by pulling the local vertices by a Gaussian weighted distance of the mouse motion d. Thus, the vertex that is at the initial position  74  of the mouse moves into position  76  the same distance d as the mouse motion d. Vertices farther away from the mouse move a shorter distance based on a Gaussian function scaling of the mouse motion. Typically, the Gaussian tool  72  is controlled by a single Gaussian radius which defines the width of the Gaussian spread. Alternatively, the Gaussian tool  72  is controlled by separate x- and y-Gaussian radii which allow for the x-radius to be used in the plane of motion of the mouse, and the y-radius to be used orthogonally to the drawing plane. In another embodiment, the Gaussian tool  72  is controlled by a function, e.g. triangle, parabola, etc., that smoothly transitions from 1 to 0 with the appropriate set of parameters to accomplish a transformation of the selected vertices. 
   In one embodiment, the Gaussian pull tool  72  pulls a Gaussian shaped distortion (or other functional shape the smoothly transitions from 1 to 0) but derives the distance that the distortion is pulled from the distance of the mouse position from the organ model. The organ model  52  is pulled directly to the mouse position enabling smooth drawing, rather than having to click up and down on the mouse to grab and stretch the organ. 
   With continuing reference to  FIG. 1  and further reference to  FIGS. 5-6 , a sphere push tool  80  searches for all vertices contained within a sphere  82  of a specified radius R around the mouse location  84 . Each vertex in the sphere  82  is moved to the surface of the sphere along the vector from the mouse location  84  through the original vertex location. As the mouse moves the push tool  80  by moving location  84 , the organ model  52  is pushed either inward or outward depending on the location of the vertex with respect to the mouse location  84 . The Sphere tool  80  is controlled by a single sphere radius parameter that is preferably stored with the individual organ model. In this way, the surface is deformed analogous to pressing a spherical tool of the selected radius against a soft clay surface. But, on the computer, the tool  80  can be placed inside the model to push out or outside to push in. Of course, other surfaces of predetermined shapes such as ellipses are also contemplated. Optionally, the model surface can be re-triangulated after the surface modification to smooth the reshaped organ surface. 
   With continuing reference to  FIG. 1  and further reference to  FIGS. 7-9 , a pencil draw tool  90  is used to deform an original boundary  92  of the organ model such that it aligns with a drawing path or actual boundary  94  of the drawing motion of the mouse. While the user uses the mouse to draw a line along the actual boundary  94  of the structure to be segmented, the model&#39;s original boundary  92  deforms to match the drawing path  94  in the plane of mouse motion. Outside of the plane of mouse motion, the organ model deforms to perform a smooth transition in the model. Rather than drawing a complete path  94 , the drawing path  94  may be approximated with a series of dots. 
   The Pencil draw tool  90  recognizes begin  96  and end  98  points of each mouse step and defines a capture plane  100  through a vector whose normal vector is in the plane of the mouse motion and is normal to the mouse motion direction. Two end planes  102 ,  104 , which are defined at the start and end points  96 ,  98 , identify a capture range  106  around the mouse motion vector. Vertices located within the capture range  106  are pulled towards the capture plane  100 . Vertices that lie on the plane  100  are pulled onto the plane  100 . Vertices that lie further from the mouse motion plane are pulled with a Gaussian weighting of the distance to the capture plane  100  based on the distance from the mouse motion plane. 
   In one embodiment, the Pencil tool  90  is used to shrink fit an organ model to a predefined set of contours for a particular organ where the mouse motion is replaced with successive vertices of the pre-defined contour. 
   Preferably, the Pencil draw tool  90  is controlled by a In-Draw Plane distance which defines the maximum distance between a vertex of the organ model and the mouse for the vertex to be captured by the Pencil tool  90 , and a From-Draw Plane parameter which dictates how the model  52  is deformed in the direction orthogonal to the drawing plane and represents the width of the Gaussian function used to weight the distance that the vertices move. In one embodiment, the Pencil draw tool  90  is controlled by a function that smoothly transitions from 1 to 0 to perform the weighting of the distance of vertex motion for vertices that do not lie on the drawing plane. 
   Optionally, the auto-segmentation process  64  is run after manual segmentation, preferably freezing the manually adjusted model surfaces against further modification or modification beyond preselected criteria. 
   The invention has been described with reference to the preferred embodiments. Modifications and alterations may occur to others upon a reading and understanding of the preceding detailed description. It is intended that the invention be constructed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.