Patent Application
20090016589
1. Technical Field
The present disclosure relates to computer-assisted detection and, more specifically, to computer-assisted detection of colonic polyps using convex hull.
2. Discussion of Related Art
Computer-assisted diagnosis (CAD) is the process of using computers to analyze medical images and automatically detects structural features that may be indicative of disease. CAD may thus combine radiographic imaging techniques and artificial intelligence to detect and classify disease in a non-invasive way.
CAD may begin with the acquisition of medical image data using one or more imaging modality. For example, images may be acquired using two-dimensional modalities such as conventional x-rays or images may be acquired using three-dimensional modalities such as computed tomography (CT) or magnetic resonance imaging (MRI). The image data may then be analyzed using one or more CAD techniques to identify regions of suspicion. Regions of suspicion may represent internal structures that have an elevated likelihood of being subject to disease.
A medical practitioner, for example, a radiologist, may then review the medical image data and the identified regions of interest to determine whether disease is present and to devise a course of treatment. Accordingly, the medical practitioner may use CAD to identify portions of the medical image that may deserve special attention.
Effective CAD may therefore lead to more efficient and accurate diagnosis of disease and may thus contribute to less costly and more accurate medical care.
One field in which CAD has been applied is virtual colonoscopy (VC). In VC, three-dimensional image data of a patient's colon is analyzed to diagnose colon and bowel disease, including polyps, diverticulosis and cancer. In conventional VC, the three-dimensional image data is rendered to produce an image of the colon from the point of view of an imaginary camera located within the lumen of the colon. The medical practitioner may then examine a virtual fly-through whereby sequential images are presented as if the imaginary camera is moved through the colon lumen. If, for example, the medical practitioner identified what might be a polyp, a conventional colonoscopy may be performed to further examine the potential polyp and, if necessary, remove it.
As applied to virtual colonoscopy, CAD may be used to highlight regions of suspicion within the rendered fly-through images. Alternatively, CAD may be used to highlight regions of suspicion in a two-dimensional image slice of the medical image data. In either case, CAD techniques may be employed to direct the medical practitioner's attention to any discovered regions of suspicion so that a diagnosis may be rendered.
Another field in which CAD has been applied to is the detection of disease within the lungs. Here, three-dimensional image data of a patient's lungs is analyzed to diagnose lung disease including pleura-attached nodules. As described above, CAD techniques may be employed to direct the medical practitioner's attention to any discovered regions of suspicion within the lungs so that a diagnosis may be rendered.
After one or more regions of suspicion have been identified, the CAD system may be designed to provide additional details concerning each identified region of suspicion. These details may be structural, statistical, and/or include any other data or characterization of the region of suspicion that may be of diagnostic interest.
A method for performing computer-assisted diagnosis includes acquiring medical image data, detecting one or more candidates within the medical image data, defining a search space around each detected candidate, calculating a convex hull for each candidate within each search space, determining a set of pixels that are located within the convex hull for each candidate within each search space, and calculating one or more properties concerning the candidates based on the sets of pixels within the convex hulls.
The medical image data may be CT image data, MR image data, ultrasound image data, or PET image data. The one or more candidates may be polyp candidates. The size of each defined search space may be based on the approximate size of a polyp.
The medical image data may include a plurality of views and the detection of the one or more candidates may be performed within each view of the medical image data. A separate search space may be defined around each detected candidate in each view. The convex hull may be calculated for each candidate within each search space within each view. The sets of pixels located within the convex hull may be determined for each candidate in each view. The one or more properties concerning each candidate may be calculated by first merging the sets of pixels of each particular candidate from all views.
The plurality of views may include a sagittal view, a coronal view, and an axial view. The one or more properties concerning the candidates may include a three-dimensional size of the candidate. The calculated one or more properties concerning the candidates may be used to render a diagnosis regarding each candidate. The medical image data may include a colon and the one or more candidates may be colonic polyp candidates.
A method for performing computer-assisted diagnosis includes acquiring a plurality of two-dimensional views of an internal structure, detecting one or more candidates within each view of the medical image data, defining a search space around each detected candidate within each view of the medical image data, calculating a convex hull for each candidate within each search space of each view of the medical image data, determining a set of pixels that are located within the convex hull for each candidate within each search space within each view of the medical image data, for each candidate, merging the set of pixels that are located within the convex hull from each view, and calculating a three-dimensional size for each candidate based on the corresponding merged set of pixels.
The plurality of two-dimensional views of the structure may be rendered from a three-dimensional medical image. The plurality of two-dimensional views may be acquired from CT image data, MR image data, ultrasound image data, or PET image data. The one or more candidates may be polyp candidates. The size of each defined search space may be based on the approximate size of a polyp. The plurality of views may include a sagittal view, a coronal view, and an axial view.
The three-dimensional size calculated for the candidates may be used to render a diagnosis regarding each candidate. The internal structure may include a colon and the one or more candidates may be colonic polyp candidates.
A computer system includes a processor and a program storage device readable by the computer system, embodying a program of instructions executable by the processor to perform method steps for performing computer-assisted diagnosis. The method includes acquiring a plurality of two-dimensional views of an internal structure, detecting one or more candidates within each view of the medical image data, defining a search space around each detected candidate within each view of the medical image data, calculating a convex hull for each candidate within each search space of each view of the medical image data, determining a set of pixels that are located within the convex hull for each candidate within each search space within each view of the medical image data, for each candidate, merging the set of pixels that are located within the convex hull from each view, and calculating a three-dimensional size for each candidate based on the corresponding merged set of pixels.
The one or more candidates may be polyp candidates and the size of each defined search space may be based on the approximate size of a polyp. The plurality of views may include a sagittal view, a coronal view, and an axial view.
A more complete appreciation of the present disclosure and many of the attendant aspects thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein:
In describing exemplary embodiments of the present disclosure illustrated in the drawings, specific terminology is employed for sake of clarity. However, the present disclosure is not intended to be limited to the specific terminology so selected, and it is to be understood that each specific element includes all technical equivalents which operate in a similar manner.
Exemplary embodiments of the present invention seek to provide a quick and accurate approach for locating and characterizing regions of suspicion from medical image data. A region of suspicion, as used herein, may mean any identified structure that is discovered to have an elevated risk of indicating the presence of disease. For example, a region of suspicion may be a structure protruding from a colon wall that appears to be polyp like. Identification of the region of suspicion may be performed by the CAD system and a medical practitioner may then review the medical image data and the identified regions of suspicion to render a diagnosis.
Medical image data, as used herein, may mean two-dimensional or three-dimensional characterizations of the internal structure or function of a patient. Medical image data may be acquired from a medical imaging device. Medical image data is generally digital data, and may be acquired either by direct digital reading or by digitization of an analog medical image.
Exemplary embodiments of the present invention may be applied to medical image data that has been acquired from any medical imaging device. For example, exemplary embodiments of the present invention may use medical image data that is a result of computed-tomography (CT), magnetic resonance (MR), ultrasound, positron emission tomography (PET), as well as medical image data from other sources.
After regions of suspicion are identified from the medical image data, a medical practitioner such as a radiologist may review the medical image data to render a diagnosis. In rendering a diagnosis, the medical practitioner may pay more careful attention to the areas identified as regions of suspicion. In this way, CAD systems may lead to faster and more accurate diagnosis of disease based on medical image data.
Besides identifying regions of suspicion, CAD systems according to exemplary embodiments of the present invention may also provide data of diagnostic significance that may be calculated from the medical imaged data and presented to the medical practitioner to help render a diagnosis. This data may pertain to the medical image data in general and/or may be particular for each identified region of suspicion. For example, where the region of suspicion is a polyp candidate, the CAD system may be used to provide an accurate assessment of the three-dimensional size of the polyp candidate. Moreover, the CAD system may also be used to characterize each region of suspicion, for example, the CAD system may be used to identify that the region of suspicion is in fact a polyp.
After medical image data has been acquired, the medical image data may be rendered in a particular view that is well suited for viewing regions of suspicion. For example, in the case of analyzing the colon, the medical image data may be arranged as either a set of two-dimensional image slices representing cross sections of the colon, or as a three-dimensional virtual fly-through. In such a case, the walls of a cross section of the colon may be visible. Any colonic polyps or other areas of potential disease may also be observable from this image view.
Examples of suitable views may include sagittal, coronal, and axial views. Multiple views may also be used, for example, to corroborate findings or to characterize a three-dimensional structure based on multiple two-dimensional views.
After the medical data has been rendered such that at least a section of the contour of the colon wall is visible, a search space may be defined, for example as described in detail below. A convex hull may be determined around the set of pixels that comprise the colon wall within the search space. The convex hull is defined as the minimal convex subset of contour points from the colon wall contour point set, where the entire cross-sectional circumference of the colon wall is in view, the convex hull may appear as a substantially circular (convex) shape where structures such as folds, polyps, etc. would be located inside the convex hull.
The pixels of the medical image data may be characterized as either foreground pixels or background pixels based on domain knowledge. For example, pixels outside of the colon lumen may be labeled as foreground pixels. After the convex hull has been calculated, foreground pixels interior to the calculated convex hull may be considered to form regions of suspicion, for example, polyp candidates.
Alternatively, if only the surface pixels of structures within the convex hull need to be identified, then only boundary pixels excluded from the convex hull boundary set of points will suffice.
The convex hull may accordingly be used to separate locally concave objects and structures from a convex overall surface such as the colon lumen. Alternatively, the convex hull may also be used to locate convex objects from concave surfaces or structures by inverting the foreground/background assignment of pixels. For example, the dots 10 that intercept the convex hull 12 may represent a normal surface of the colon while the dots 14 located within the convex hull 12 may represent folds, polyps, etc. and may thus be considered part of a region of suspicion. Accordingly, a region of suspicion, for example a polyp candidate, may be segmented and/or characterized with the help of the convex hull.
A single region of suspicion may be analyzed according to the convex hull in multiple views. The multiple views may include views from multiple angles, a sequence of two-dimensional image slices, or may include successive frames in a virtual fly-through rendering. At each view, information concerning the shape of the region of suspicion may be acquired. The segmentation and shape information from the multiple views may then be combined to obtain a three-dimensional segmentation of the region of suspicion and/or estimations of other features such as the three-dimensional size of the region of suspicion.
Exemplary embodiments of the present invention are described herein with reference to the detection and characterization of polyps within the colon; however, the invention is not limited thereto. Exemplary embodiments of the present invention may be applied to identifying, segmenting and characterizing regions of suspicion within any convex structure at a scale larger than that of the locally concave region of interest, such as substantially spherical, ellipsoidal, or tubular structures.
When exemplary embodiments of the present invention are applied to the detection and characterization of colonic polyps, useful views may include sagittal, coronal, and axial views.
According to exemplary embodiments of the present invention, a CAD system may be used to detect and/or characterize colonic polyps.
Next, for each polyp candidate, the medical image data may be rendered into one or more views (Step S53). The views may each show the polyp candidate from a different angle. For example, sagittal, coronal, and axial views may be rendered.
Then, one or more appropriately sized search spaces may be defined around each polyp candidate in each view (Step S54). Each search space may be centered on the identified points for each view. The size of the search space may be chosen to accommodate a polyp of a particular size. According to one exemplary embodiment, multiple search spaces of varying sizes may be defined for each candidate location. For each search space, the convex hull may be calculated and the three-dimensional size for each candidate may be determined, for example, according to the approaches discussed above.
The choice of search space size may be important in performing convex hull analysis. This is because the entire colon wall cross section may not be visible from every plane and thus, when looking at only a subsection of the colon wall that may include a polyp, it may be difficult to distinguish between the curvature of the colon wall and the curvature of the polyp if the search space is defined too small. If the search space is defined too big, other nearby structures, such as folds, may be included and would have to be separated from the polyp by the performance of additional processing steps.
Accordingly, the correct search analysis may be performed using multiple search spaces or an appropriately sized search space may be selected. In making the determination as to the desired size of the search area, assumptions as to the size and protrusion of an actual polyp are taken into account. For example, polyps may be known to be within a particular range of sizes and may be known to protrude to some degree into the lumen of the colon.
After the search spaces have been defined around the candidate locations for each view, the contour line of the colon wall may be determined (Strep S55). The contour of the colon wall may be defined as the set of surface pixels within the search space. For example, pixels of the colon wall that are adjacent to a pixel belonging to the lumen may be characterized as part of the surface pixels. Alternatively, a detagging algorithm may be used to determine pixels (voxels) on the colon wall when contrast material is present in the colon. Additionally, pixels on the border of the search space are be characterized as surface pixels where the colon wall is not found.
After the contour line has been determined, convex hull processing may be performed (Step S56) for one or more image planes within the search space such that all pixels of a given plane that fall interior to the polygon defined by the convex hull may be identified (Step S57). This may be repeated for each image plane and/or image view within each three-dimensional search space. The result of this approach may be a set of pixels within the convex hull from multiple planes.
After calculating the convex hull for each image plane in the search space and for each view (for example, the sagittal view, the coronal view, and the axial view) and determining all pixels that are located within the convex hull, the located pixels for each candidate location may be merged (Step S58). Merging may be performed, for example, by taking the union of all results or by selecting only those pixels that were inside the convex hull in at least some number of views, for example, two views or three views.
After the results of the multiple views have been merged, additional statistics may be derived from the segmented volume (Step S59). All of the pixels that fulfill the merging criteria may form a three-dimensional cloud of pixels. Statistics such as maximum distance between pixels, maximum extension, connectivity, shape, etc. may then be derived from each cluster. Additionally, statistical information pertaining to the segmented objects may include curvature analysis, size analysis, derivative analysis, and/or any other form of shape description analysis.
The statistical results of this step may then be presented to the medical practitioner, for example, along with other results of the CAD processing such that the medical practitioner may be able to use the statistical results to aid in rendering a diagnosis. Alternatively, the statistical results may be used by the CAD system to render an automatic diagnosis.
The computer system referred to generally as system 1000 may include, for example, a central processing unit (CPU) 1001, random access memory (RAM) 1004, a printer interface 1010, a display unit 1011, a local area network (LAN) data transmission controller 1005, a LAN interface 1006, a network controller 1003, an internal bus 1002., and one or more input devices 1009, for example, a keyboard, mouse etc. As shown, the system 1000 may be connected to a data storage device, for example, a hard disk, 1008 via a link 1007.
Exemplary embodiments described herein are illustrative, and many variations can be introduced without departing from the spirit of the disclosure or from the scope of the appended claims. For example, elements and/or features of different exemplary embodiments may be combined with each other and/or substituted for each other within the scope of this disclosure and appended claims.
The present application is based on provisional application Ser. No. 60/948,764, filed Jul. 10, 2007, the entire contents of which are herein incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 60948764 | Jul 2007 | US |
