Forming three dimensional objects using a decision rule in medical image data

Information

  • Patent Application
  • 20070297659
  • Publication Number
    20070297659
  • Date Filed
    June 21, 2006
    19 years ago
  • Date Published
    December 27, 2007
    18 years ago
Abstract
A decision rule is used that examines the computer-aided detected (CAD) regions of interest in a computed tomography (CT) slice pair taken from volumetric medical CT scan to determine whether the detected regions of interest are part of the same object is disclosed. Segmentation is performed after initially detecting a region of interest but before calculating features in order to refine the boundaries of the detected regions of interest. Segmentation occurs in the two-dimensional slices by segmenting the region of interest on each slice. Adjacent slices are examined to determine if adjacent objects are actually part of the same structure. If they are not, the objects are split apart. In this way, three-dimensional objects are formed from two-dimensional segmentations.
Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The following detailed description of specific embodiments of the present invention can be best understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals and in which:



FIG. 1 is a flow chart illustrating segmentation according to an embodiment of the present invention.



FIG. 2 is a flow chart for splitting objects that do not belong to the same object according to an embodiment of the present invention.





DETAILED DESCRIPTION

In the following detailed description of the embodiments, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration, and not by way of limitation, specific embodiments in which the invention may be practiced. It is to be understood that other embodiments may be utilized and that logical, mechanical and electrical changes may be made without departing from the spirit and scope of the present invention.


With CT Lung CAD and CT Colon CAD, segmentation occurs after initial regions of interest are detected, but before features are calculated. The detector finds the regions of interest. After detection, segmentation occurs by taking those regions of interest and refining their boundaries. The segmentation first operates in two dimensions by segmenting the region of interest on each CT slice. As a second step, objects detected on adjacent CT slices are combined to form three-dimensional objects. Accurate segmentations allow for accurate features leading to good classification of suspicious features.


Referring initially to FIG. 1 which is a general overview of the steps taken during segmentation of a CT slice pair, the first step 110 is to have or create two-dimension segmentations of each CT slice. Segmentation occurs as described above and as is commonly known in the art. The next step 120 determines whether the two-dimensional segmentations on adjacent CT slices should be grouped together as a one single object to form three-dimensional segmentations (step 130) or that the two-dimensional segmentations actually belong to different objects and should be split apart as described below.



FIG. 2 is a flow chart detailing the steps for determining which two-dimensional segmentation should be grouped together to form three-dimensional segmentations. For purposes of this embodiment, in step 200, all objects are formed by two-dimensional segmentations with simple connectiveness in three-dimensions and the objects are all considered part of the same object. Incorrectly joined slices will be subsequently identified in later steps and split.


All of the formed objects are placed into a list in step 210. Then, all of the CT slices in the objects in the list will be examined successively for potential splitting, starting with the first CT slice in the first object in the list. As the objects are split, the newly formed split-off objects will be added to the end of the list.


Splitting automatically occurs when an object has a slice with a single two-dimensional object that overlaps multiple objects on an adjacent CT slice. With this in mind, each object is examined in step 220 to determine if the object starts with a CT slice containing a single object or CT slice containing multiple objects. If the object starts with one or more CT slices with multiple two-dimensional objects on the CT slice, those CT slices are split from the original object and moved to the end of the object list in step 230. Processing of the object resumes in step 240.


In step 240, features and measurements are calculated using the two-dimensional segmentations on adjacent CT slices containing a single object. In this embodiment, only one feature is used. That feature is the measure of the mutual overlap of the two-dimensional segmentations on the adjacent CT slices. These computed features are used by a decision rule of classifier in step 250 to make a determination as to whether each pair of CT slices constitutes parts of the same object. If the two-dimensional segmentations on adjacent CT slices are determined to be parts of different objects, those two-dimensional segmentations are split apart and placed at the end of the object list.


The splitting determination occurs in step 260. For this embodiment, the decision rule may have a simple threshold of about 0.4 on the mutual overlap feature. If the mutual overlap of the two-dimensional segmentations is greater than 0.4, the objects on the adjacent slices are assumed to be from the same object. Again, slices that contain multiple two-dimensional objects are automatically split. When the objects are split in step 270, the split-off object is placed at the end of the object list and the next object is retrieved from the list in step 300.


When the split occurs, or after all of the CT slices of the current object have been examined (steps 280 and 290 loop over all of the CT slices), the analysis moves to the next object in the list (steps 300 and 310). When all objects have been examined, the splitting process is complete. The output, step 320, is a list or labeled mask of three-dimensional segmentations. Therefore, some objects that would have been previously joined incorrectly using simple connectiveness will no longer be considered the same object.


It is noted that terms like “preferably,” “commonly,” and “typically” are not utilized herein to limit the scope of the claimed invention or to imply that certain features are critical, essential, or even important to the structure or function of the claimed invention. Rather, these terms are merely intended to highlight alternative or additional features that may or may not be utilized in a particular embodiment of the present invention.


Having described the invention in detail and by reference to specific embodiments thereof, it will be apparent that modifications and variations are possible without departing from the scope of the invention defined in the appended claims. More specifically, although some aspects of the present invention are identified herein as preferred or particularly advantageous, it is contemplated that the present invention is not necessarily limited to these preferred aspects of the invention.

Claims
  • 1. A method of forming three dimensional objects in medical imagery, the method comprising: detecting a region of interest in a two-dimensional CT slice using a CAD system;segmenting the region of interest from each CT slice;examining the segmented regions of interest on adjacent CT slices to determine whether the segmented regions of interest are part of a same object; andcombining objects on adjacent CT slices to form three-dimension objects.
  • 2. The method of claim 1, wherein the medical imagery is taken from the lungs.
  • 3. The method of claim 1, wherein the medical imagery is taken from the colon.
  • 4. The method of claim 1, further comprising: examining the segmented regions of interest to determine if any adjacent CT slices are incorrectly joined.
  • 5. The method of claim 4, further comprising: splitting apart incorrectly joined objects on adjacent CT slices.
  • 6. The method of claim 4, further comprising: splitting apart incorrectly joined objects on adjacent CT slices along the Z-direction.
  • 7. The method of claim 4, further comprising: splitting apart incorrectly joined objects on adjacent CT slices using a decision rule.
  • 8. A method of forming segmented three dimensional objects in CT medical imagery, the method comprising: creating a two-dimensional segmentation of each CT slice of the CT medical imagery;determining whether the two-dimensional segmentation on adjacent CT slices are from the same object; andgrouping two-dimensional segmentation on adjacent CT slices from the same object to form three-dimensional segmented objects.
  • 9. The method of claim 8, further comprising: splitting apart adjacent CT slices if the two-dimensional segmentation on adjacent CT slices are not from the same object.
  • 10. The method of claim 8, further comprising: automatically splitting CT slices if a single two-dimensional segmentation on one CT slice has multiple overlapping objects on the adjacent CT slice.
  • 11. The method of claim 8, further comprising: measuring the mutual overlap of the two-dimensional segmentation of adjacent CT slices.
  • 12. The method of claim 8, wherein the step of determining utilizes a decision rule to decide whether the two-dimensional segmentation on adjacent CT slices are from the same object.
  • 13. The method of claim 12, wherein the decision rule is a threshold value of the mutual overlap of the two-dimensional segmentation of adjacent CT slices.
  • 14. The method of claim 13, wherein the two-dimensional segmentation of adjacent CT slices are from the same object is the mutual overlap is greater than the threshold value of the decision rule.
  • 15. The method of claim 13, wherein the threshold value for mutual overlap is about 0.4.
  • 16. The method of claim 8, further comprising: examining all of the CT slices in the CT medical imagery.
  • 17. The method of claim 8, further comprising: outputting a list of three-dimensional segmented objects after all two-dimensional CT slices have been examined
  • 18. A method of outputting a labeled mask of three-dimensional segmentations, the method comprising: forming objects from two-dimensional CT slices;placing all formed objects into a list;examining each two-dimensional CT slice in an formed object to determine if the formed object starts with a two-dimensional CT slice containing a single object or a two-dimensional CT slice containing multiple objects;automatically splitting the multiple object CT slices from the formed object and moving the multiple object CT slices to the end of the list;measuring the mutual overlap of adjacent two-dimensional CT slices to determine if the adjacent CT slices contain the same object;splitting the adjacent two-dimensional CT slices from the object and moving the two-dimensional CT slices to the end of the list if the mutual overlap is less than a threshold value; andoutputting a list of three-dimensional segmentations after all two-dimensional CT slices of all the formed objects in the list have been examined.
  • 19. The method of claim 18, further comprising: detecting regions of interest in the two-dimensional CT slices using a CAD system.
  • 20. The method of claim 18, further comprising: applying a decision rule to determine whether each pair of CT slices constitute parts of the same object.
  • 21. The method of claim 18, wherein the step of splitting the adjacent two-dimensional CT slices is along the Z-direction.
  • 22. The method of claim 18, wherein the step of forming the objects uses simple connectiveness.
  • 23. The method of claim 18, wherein the threshold value for mutual overlap is about 0.4.