Patent Publication Number: US-9898825-B2

Title: Segmentation of magnetic resonance imaging data

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation application of U.S. patent application Ser. No. 14/399,796, entitled “Segmentation of Magnetic Resonance Imaging Data,” filed on October November 7, 2014, which claims priority under 35 USC § 119(e) of U.S. Provisional Patent Application No. 61/644,518 filed on May 9, 2012, the entire disclosures of which are hereby expressly incorporated by reference herein. 
    
    
     TECHNICAL FIELD 
     The present invention relates to the field of image segmentation, and more particularly, to the segmentation of MRI data as received from an MRI apparatus. 
     BACKGROUND OF THE ART 
     Computer production of images from magnetic resonance has become an invaluable tool in medicine as the structural and biochemical information that can be obtained is helpful in the diagnosis of abnormalities without the possibly harmful effects of x-rays or gamma rays. Magnetic Resonance Imaging (MRI) provides better contrast between normal and diseased tissue than those produced by other computer-assisted imagery. 
     The final image product of an MRI must first go through a process of segmentation, which refers to the partitioning of a digital image into multiple segments in order to provide an image that is more meaningful or easier to analyze. Objects and boundaries in the image, such as lines, curves, and others, are located and enhanced using shared pixel characteristics, such as color, intensity, or texture. Bones, cartilage, ligaments, and other soft tissues of the body thus become identifiable by the trained eye. 
     While there exists many different techniques for segmenting MRI images, the quality of the output is only as good as the processing methods. There is therefore a need to improve on existing processing methods in order to provide a better output. 
     SUMMARY 
     There is described herein an image segmentation technique using an iterative process. A contour, which begins with a single point that expands into a hollow shape, is iteratively deformed into a defined structure. As the contour is deformed, various constraints applied to points along the contour to dictate its rate of change and direction of change are modified dynamically. The constraints may be modified after one or more iterations, at each point along the contour, in accordance with newly measured or determined data. 
     In accordance with a first broad aspect, there is described a computer-implemented method for segmenting magnetic resonance imaging (MRI) data, the method comprising: determining an initial position on an image for a given structure; converting the initial position into an initial contour within the given structure; and iteratively deforming the initial contour to expand into a shape matching the given structure by dynamically applying a set of constraints locally to each point along the initial contour and updating the set of constraints after one or more iterations. 
     In accordance with a second broad aspect, there is described a system for generating segmented data from magnetic resonance imaging (MRI) data, the system comprising: at least one computer server communicable with at least one computing device over a network, the at least one computer server having a processor and a memory; an initial position module stored on the memory and executable by the processor, the initial position module having program code that when executed, determines an initial position on an image for a given structure and converting the initial position into an initial contour; and a contour definition module stored on the memory and executable by the processor, the contour definition module having program code that when executed, iteratively deforms the initial contour to expand into a shape matching the given structure by dynamically applying a set of constraints locally to each point along the initial contour and updating the set of constraints after one or more iterations. 
     In accordance with a third broad aspect, there is described a computer readable medium having stored thereon program code executable by a processor for generating segmented data from magnetic resonance imaging (MRI) data, the program code executable for: determining an initial position on an image for a given structure; converting the initial position into an initial contour within the given structure; and iteratively deforming the initial contour to expand into a shape matching the given structure by dynamically applying a set of constraints locally to each point along the initial contour and updating the set of constraints after one or more iterations. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Further features and advantages of the present invention will become apparent from the following detailed description, taken in combination with the appended drawings, in which: 
         FIG. 1  is a flowchart illustrating an exemplary method for segmenting MRI data; 
         FIG. 2  is a flowchart illustrating an exemplary embodiment for segmenting images; 
         FIGS. 3 a  and 3 b    are flowcharts illustrating an exemplary embodiment for setting a starting point for contour definition along a first plane when segmenting an image; 
         FIG. 4  is a flowchart illustrating an exemplary embodiment for computing an initial position on an image for a given structure; 
         FIG. 5  is a flowchart illustrating an exemplary embodiment for performing contour definition of a structure for images taken along a given direction of the body; 
         FIG. 6  is a flowchart illustrating an exemplary embodiment for deforming a contour to define a structure in the image; 
         FIG. 7  is a flowchart illustrating an exemplary embodiment for dynamically applying a normal force when deforming a contour; 
         FIGS. 8 a  to 8 f    are illustrative screenshots of a contour being progressively deformed to define a tibia; 
         FIG. 9  is a flowchart illustrating an exemplary embodiment for meshing images; 
         FIG. 10  is an exemplary rendered points cloud of the head of a tibia; 
         FIG. 11 a    is an exemplary meshed 3D model of the head of the tibia, in its raw form; 
         FIG. 11 b    is the exemplary meshed 3D model of  FIG. 11 a    after smoothing and refinement of the model; 
         FIG. 12  is a flowchart illustrating an exemplary embodiment for setting a starting point for contour definition along a second plane when segmenting an image; 
         FIG. 13  is a flowchart illustrating an exemplary embodiment for merging segmentations along a first and a second plane to obtain a segmentation along a third plane; 
         FIG. 14  is a block diagram an exemplary system for segmenting MRI data received via a network; 
         FIG. 15  is a block diagram showing an exemplary application running on the processor of  FIG. 14 , for segmenting the MRI data; 
         FIG. 16  is a block diagram showing an exemplary segmenting module from  FIG. 15 ; and 
         FIG. 17  is a block diagram showing an exemplary contour definition module from  FIG. 16 . 
     
    
    
     It will be noted that throughout the appended drawings, like features are identified by like reference numerals. 
     DETAILED DESCRIPTION 
     Referring to  FIG. 1 , there is described a high level process  100  for providing images using MRI. The broad steps of data acquisition  102 , image segmentation along a first plane (or direction)  104 , meshing of images segmented along the first plane  106 , outputting of meshed images along the first plane  108 , image segmentation along a second plane (or direction)  110 , merging of segmentations along the first and second planes  112  to create a single structure in the image, and outputting images segmented along a third plane (or direction) are illustrated. Data acquisition  102  refers to the acquisition of MRI data using an imaging apparatus based on magnetic resonance. A magnetic field aligns the magnetization of atomic nuclei in the body, and radio frequency fields systematically alter the alignment of this magnetization. This causes the nuclei to produce a rotating magnetic field detectable by a scanner. The scanner records the information and constructs an image of the scanned area of the body. The images are recorded as a series of slices as a volume is scanned, with scanning settings determining the thickness of each slice and the spacing between slices. A 3D sequence, often used to image bones, corresponds to having no or negligible spacing between the slices. 
     When a technician or operator manipulates the apparatus in order to acquire data  102 , a virtual box may be defined around a part of the body targeted for imaging. A sequence may be selected from a plurality of available sequences in order to correspond with a desired output. The apparatus is then activated and the data is acquired in an automated fashion. 
     The data acquisition  102  may be done along one or more planes or directions throughout the body part, such as sagittal, coronal, and transverse. In some embodiments, multiple orientations are performed and the data may be combined or merged during the processing phase. For example, a base set of images may be prepared on the basis of data acquired along a first plane, such as the sagittal plane, with missing information being provided using data acquired along a second plane, such as the coronal plane. It should be understood that, although the coronal plane is discussed herein as being the second plane, the transverse plane or any other suitable plane may also be used as the second plane. Other combinations or techniques to optimize the use of data along more than one orientation will be readily understood by those skilled in the art. In some embodiments, a volume of data is obtained using a 3D acquisition sequence independent of an axis of acquisition. The volume of data may be sliced in any direction as desired. 
     The data may be acquired using any known magnetic resonance imaging techniques and any known devices for acquiring such data. The acquired data may be provided in various known formats and using various known protocols, such as Digital Imaging and Communications in Medicine (DICOM), for handling, storing, printing, and transmitting information. Other exemplary formats are GE SIGNA Horizon LX, Siemens Magnatom Vision, SMIS MRD/SUR, and GE MR SIGNA 3/5 formats. 
     Step  102  may also comprise acquiring data from a parameter file, which illustratively comprises a set of parameters to be taken into account and/or used as input when implementing the process  100 . For instance, the parameter file may comprise data indicative of a minimal allowable size for holes between edges present in the image(s). As known to those skilled in the art, the edges may correspond to sudden transitions in the image gradient and may represent boundaries of objects or material properties. The parameter file may also comprise data defining contour curvature constraints. It should be understood that the parameter file may comprise other data and/or parameters and that the latter may or may not be specific to an individual, e.g. a patient, whose body is being imaged using the process  100 . Indeed, in one embodiment, the data contained in the parameter file may be static throughout the process  100 . In other embodiments where the parameter file is specific to an individual, the parameter file may be modified to be tailored to the individual in question. 
     Once acquired, the images are segmented  104  along a first plane, e.g. the sagittal plane, in order to identify the different structures in each slice.  FIG. 2  illustrates an exemplary method for segmenting the acquired images  104 . Step  200  consists in determining a starting point for contour definition of a given structure in a set of images along the first plane. As will be discussed further below, a step  201  of determining a starting point for contour definition of a given structure in a set of images taken along the second plane, e.g. the coronal plane, is also implemented when segmenting (at step  110 , discussed further below) images along the second plane. In some embodiments, the starting point set at step  200  may be a single point. The initial contour may then be four neighboring points (or pixels) around the single point. From the starting point, contour definition of a structure  202  is performed for a given slice. When more than one structure is present in a given slice (or image)  204 , the step of performing contour definition  202  is repeated until all structures have been defined. Contour definition of a structure  202  may be performed on all subsequent images in the data set  206 . Once all images have been processed, segmentation is complete  208 . 
     In one embodiment, the images (or slices) are processed sequentially, one at a time. In alternative embodiments, the images may be processed in parallel. For example, in one alternative embodiment, a first center slice is processed and the images from the center slice to the first slice are processed in parallel with the images from the center slice to the last slice. In a set of 100 exemplary slices, slice 50 is processed initially in accordance with steps  200  to  204  of the segmentation process  104 . Slices 49 and 51 are then processed in parallel, followed by slices 48 and 52, followed by slices 47 and 53, etc, until slices 1 and 100 are processed. In another alternative embodiment, the set of 100 exemplary slices are separated into five groups of 20 slices and each set of 20 slices is processed in parallel. In yet another alternative embodiment, all even-numbered slices from the set of 100 exemplary slices are processed together, in parallel, followed by all of the odd-numbered slices. Parallel processing during segmentation reduces the overall time required to generate segmented images. It also prevents errors from being propagated throughout the set of slices, should there be errors introduced during any of the step of defining contours of the structures  202  in each image. When performing step  200 , slices need to be processed sequentially, however smaller blocs of sequential slices may be processed in parallel. When performing step  202 , the order of the slices has no impact on the result. 
     In the embodiment illustrated in  FIG. 2 , the starting points for contour definition of all of the structures in a given slice are calculated before proceeding to the step  202  of defining the structure contours. Referring now to  FIG. 3 a   , there is illustrated an exemplary embodiment for determining  200  a starting point for contour definition of a structure in a set of images along the first plane. The method generally comprises some image processing steps  301 , followed by the computation of an initial position on the center image  302 , and followed by the parallel computation of an initial position on a current image of a first half of the slices  303  and a current image of a second half of the slices  304 . The initial position is converted to an initial contour  305 .  FIG. 3 b    is an exemplary embodiment for the image processing steps  301 , whereby anisotropic filtering  402  and edge detection  404  are performed on the images before computing the initial position on the center image  302 . 
       FIG. 4  illustrates an exemplary embodiment for the computation of an initial position on the central image  302 . The steps illustrated in  FIG. 4  may be generally referred to as further processing of the acquired data using various known techniques such as filtering and edge detection. Anisotropic filtering  502 , cropping of the image  504 , edge detection  506 , are performed to set-up the morphological dilation of skin  508  and mask region growing  510 . Subsequent to edge detection  506 , an edge image is obtained, in which information that may be considered of low relevance has been filtered out while preserving the important structural properties of the original image. In particular, the edge image may comprise a set of connected curves that indicate the boundaries of image structures as well as curves that correspond to discontinuities in surface orientation. 
     Processing of the bones and skin may be performed in parallel, as illustrated, or sequentially. When processing the skin, boundaries are extracted  512  after morphological dilatation of the skin  508 . When processing the bones, once edges are detect  506 , mask region growing  510  may be performed. With mask region growing  510 , contours are grown from the initial point to adjacent points by applying a mask of a given size, e.g. n×n pixels. The number n of pixels of the mask may be selected so as to optimize the result achieved by the image segmentation process  100 . In particular, the number n of pixels may be chosen on the basis of data found in the parameter file discussed above and may be varied in accordance with the image resolution. For instance, when it is desired for an evolving contour not to fall into holes between edges, the number n of pixels may be set in accordance with the minimal allowable size of the holes. In one embodiment, the size of the mask may be set such that the number n is greater than one (1) and particularly between ten (10) and twenty (20) pixels. Other sizes may apply. By properly setting the size of the mask used for region growing, it becomes possible to precisely tune the overall segmentation process. For example, an evolving contour can be prevented from entering within an area where adjacent structures in the image contact one another by only one (1) pixel. 
     Still referring to  FIG. 4 , various clean-up or refinement steps may then be performed including removing small objects from the image  514  and/or removing objects along image borders  516 . Region filling  518  may be performed for the skin processing, and a leg mask may be applied  520  to the bone processing (when imaging a leg). The two sets of data, e.g. data for the bones and skin, are merged. Any object outside the vertical axis of the largest object may then be removed  522 . For this purpose, a vertical box may be defined around the largest object in the image (e.g. around the femur bone) so as to encompass the vertical axis of the object. Objects that lie outside of the vertical box may then be removed because such objects are not of interest for segmentation. First and second largest objects, which illustratively correspond to the tibia and femur structures, are then kept  524  while the rest may be discarded. The object centers of the tibia and femur bone structures may then be computed  526 . It should be understood that, depending on the structures being segmented, more than the two (2) largest objects may be kept at step  524 . For instance, the third largest object, which may correspond to the patella, may also be kept. The patella center may then also be computed and selected at step  526 . Also, this method may differ slightly when imaging a part of the anatomy other than a leg. Thus, any other suitable refinement steps may apply. 
     In some embodiments, the steps of  FIG. 4  are performed only on the first or initial slice of a set of slices. As indicated above, the first or initial slice may be the center slice from a set of slices representing a volume of a body part. When computing the initial position for a subsequent slice, this may be done by radial sampling around a previous position until a first intersection with an edge occurs. A mean position of all intersection points may then act as the initial position for the structure on the new slice. 
       FIG. 5  illustrates an exemplary embodiment for defining the contour of the structure  202  from the starting point found in step  200 , for any given structure. Image pre-processing step(s)  602  may precede the subsequent steps of the method  202 . Some exemplary pre-processing steps may include, but are not limited to, computing the magnitude of a gradient and clipping image intensity based on the gradient magnitude, applying an anisotropic filter and performing edge detection and edge linking, removing edges based on the mean gradient magnitude along the edge, and rebuilding the image from an edges list. Image pre-processing  602  is followed by a computation of the vector field  604 . This may be done using various known methods, such as the Gradient Vector Flow (GVF) method. An initial contour normal is then computed  606  in order to perform contour deformation  608 . This step may be performed iteratively as a function of a predetermined parameter. 
     In the embodiment illustrated, a mean distance between a current contour and a previous contour is compared to a threshold D min    616 . The threshold value D min  illustratively represents the tolerance on the rate of change of the contour&#39;s distance. If the mean distance exceeds the threshold value, then the contour deformation step  608  is repeated. If the mean distance is smaller than the threshold value, then it can be determined that the current contour has expanded sufficiently and closely matches the structures that were to be segmented. The deformation of the contour can therefore be stopped and the process ends  618 . Other predetermined parameters, such as a variation of the mean distance on multiple iterations, may also be used to determine if the contour should be further deformed  608 . When using D min  as a parameter, the distance of the present contour from the previous contour  610  is evaluated and auto-intersections of the contour may be removed  612 . The contour normal may then be updated  614  before proceeding with the comparison step  616 . 
     Prior to proceeding with updating the contour normal  614 , the size of the contours may be regularized (not shown). Indeed, once each contour has been deformed at step  608 , the size of the contour, and accordingly the spacing between points thereof, increases. It is therefore desirable to adjust the size of the contour so as to harmonize the spacing between contour points. In particular, this may involve computing the distance between adjacent points on the current contour, and more particularly the distance between a point on the current contour and its closest neighbor. The computed distance may then be compared to a predetermined threshold distance, e.g. 0.5 pixels, to determine whether the computed distance is above the threshold. If this is not the case, i.e. the computed distance is below or equal to the threshold, this implies that the contour size has not changed beyond the acceptable tolerance. The next step may then be to proceed with updating the contour normal  614 . Otherwise, if the computed distance is above the threshold, this implies that the size of the contour has increased beyond the acceptable tolerance and that harmonization of the contour&#39;s size is required. In order to adjust the contour size, additional points may be inserted between the adjacent contour points. Although not illustrated, it should be understood that points may also be removed between adjacent contour points if the distance between the adjacent contour points is too low compared to the threshold distance. 
     In one embodiment, the contour deformation step  608  is performed for a predetermined number N of iterations prior to steps  610  to  616  being performed. In this case, constraints, which, as will be discussed further below, are dynamically applied to each point along the contour for deforming the latter, are then updated after N iterations of the contour deformation step  608 . In addition, after it is determined at step  616  that the mean distance is lower than the threshold value, the process  202  may only end  618  after steps  608  to  616  have been repeated for a predetermined number M of iterations. 
     Referring now to  FIG. 6 , there is illustrated an exemplary embodiment for performing contour deformation  608 . In the embodiment illustrated, deformation is performed using a set of dynamically set constraints at each point along the contour. The constraints illustrated are deformation constraints, viscosity constraints, and/or form constraints. 
     Deformation constraints refer to the continuity and curvature at each point along the contour. For each point, a distance to a nearest edge is computed  702 . The locally computed distances may then be used to dynamically determine the deformation constraints to be applied at each point along the contour  704 , for a given iteration of contour deformation. After each iteration, at each point along the contour, the constraint may be modified independently from those of a neighboring point (or any other point along the contour) based on a newly computed distance with a nearest edge. In some embodiments, computed distances from one or more neighboring points along the contour may be taken into account when dynamically modifying the constraint for any given point. This allows the constraint to be relaxed when approaching an edge, compared to points along the contour that remain far away from edges. 
     The viscosity constraint refers to a parameter used to avoid having an expanding contour enter into holes between edges. This is done by setting a threshold parameter for a distance between two edges. If the distance between the edges is smaller than the threshold parameter, the contour is not allowed to enter the space during its deformation at that point. During the deformation process, the magnitude of the vector field at each point along a contour is evaluated  706 . For zones where the magnitude is lower than a given parameter, spacing or distance between edges is measured and the normal force applied at those points, i.e. the force that is normal to the contour at each contour point, may be reduced in order to avoid having the contour enter a small hole between edges  708 . 
     The form constraints refer to imposing certain constraints to pixels locally as a function of expected shapes being defined and the position of a given pixel within the expected shape. For example, if the structure being defined is a femur bone, then a point along a contour defining the bottom end of the femur may be treated differently than a point along a contour defining the top end of the femur. Since the top end is much larger, the restrictions applied to the point on the bottom end contour differ from the restrictions applied to the point on the top end contour. For example, if the structure to be segmented has the form of a vertical cylinder, as is the case of the cartilage at the top end of the femur, the form constraint may be used to reduce the displacement of the contour in the horizontal, i.e. X, direction and to force the contour to move in the vertical, i.e. Y, direction only. The form constraint may further specify that no more than 50% of the displacement of contour points is to be performed in the X direction than in the Y direction. The form constraint may therefore modify the displacement vector of the contour so as to increase or decrease the contour&#39;s displacement strength in a given direction. In order to apply form constraints, various form constraint zones are defined and contour points present in the form constraint zones are identified  710 . This allows the form constraints to be applied  712  as a function of the position of the pixel and/or the form constraint zone in which it sits. Application of the form constraints may comprise applying variable forces on x and y components of a displacement vector as a function of position in the structure. 
     Referring now to  FIG. 7  in addition to  FIG. 6 , the next step  714  may be to dynamically apply the normal force. Step  714  illustratively comprises predicting at step  718  the displacement direction of contour points relative to the contour normal. The displacement direction of each contour point is illustratively perpendicular to the contour normal at a given contour point. A displacement direction may be associated with a contour point if it is in range of gradient influence of this point. For this purpose, rays may be projected in the normal direction at point along the contour. Edges present in the displacement direction may then be identified at step  720  by determining intersection points between the projected rays and adjacent edges. It then becomes possible to discriminate between edges of interest, i.e. real edges that delineate the boundary of a structure, and noise using a priori knowledge. 
     The a priori knowledge may be gained from the displacement of contour points adjacent to the given contour point. During deformation of the contour, all contour points illustratively evolve towards edges in the edge image and stop once they reach an edge. The edge at which each contour point stops may either be an edge of interest, e.g. a long edge, or noise, e.g. a short and/or folded edge, as discussed above. When an edge is of interest, i.e. long, most contour points will tend to evolve towards this edge at each deformation iteration and eventually stop thereat. However, when an edge is noise, i.e. short, fewer contour points tend to evolve towards the edge and stop thereat. Using this a priori knowledge, and more particularly a relationship between each edge and contour points having knowledge thereof, e.g. contour points having evolved towards the edge, it becomes possible to discriminate between edges. Thus, step  720  can be used to forecast whether important edges are in the displacement direction. The evolving contour may then be prevented from stopping at false short edges, i.e. noise, thereby accurately expanding the contour within the structure to be segmented. 
     Once the displacement direction has been predicted at step  718  and edges in the displacement direction identified at step  720 , the normal force may be dynamically modified at step  722 . In particular, the normal force may be modified according to the distance between a point on the current contour and edges in the edge image, as computed at step  702  of  FIG. 6 . The normal force is indeed adjusted so that the magnitude of the displacement of the contour point is not so high that the contour, once deformed from one iteration to the next, is displaced beyond a given edge. For this purpose, the normal force may, for example, be dynamically modified so as not to apply to all contour points and/or have a maximum magnitude for all deformation iterations. 
     The normal force may also be adjusted to avoid having the expanding contour enter into holes between edges. This may be done using a threshold parameter for a distance between two edges, as retrieved from the parameter file discussed above. If the distance between the edges is smaller than the threshold parameter, the contour is not allowed to enter the space between the edges during its deformation at that point. During the deformation process, the magnitude of the vector field at each point along a contour is evaluated at step  604  of  FIG. 5 . For zones where the magnitude is lower than a given parameter, spacing or distance between edges is measured and the normal force applied at those points may be reduced in order to avoid having the contour enter a small hole between the edges. 
     Alternatively, holes may be detected according to the distance between each contour point and the edges, as computed at step  702  of  FIG. 6 . In particular, holes may be detected by identifying adjacent points on the current contour, which are close to edges. For instance, for a contour comprising fifty (50) points numbered from 1 to 50, contour points 10 to 20 may be identified as being close to a first edge and points 24 to 30 as being close to a second edge while contour points 21 to 23 are close to neither the first nor the second edge. As such, it can be determined that points 21 to 23 are positioned nearby a hole of size two (2) units between the first edge and the second edge. Having detected this hole, the current contour can be prevented from entering therein by adjusting at step  722  the normal force applied to contour points 21 to 23 accordingly. A threshold may also be associated with the size of the detected holes. In particular, gaps between edges that are lower than a predetermined threshold may be considered as holes while gaps that are above the threshold may not. For instance, the threshold may be set to a size of ten (10) points. The gap between points 21 to 23 having a size of three (3) points, which is lower than ten (10), the gap can be identified as a hole. In this embodiment, the normal force may not be used to prevent the contour from entering gaps that are under the threshold size, e.g. holes. 
     Referring back to  FIG. 6 , the deformation is finally performed  716  using all of the set parameters. In some embodiments, not all of the above-described constraints are applied. For example, the form constraints may be applied only for certain shapes, or for pixels in certain positions of certain shapes. Also for example, early iterations may use only deformation constraints while later iterations may use deformation constraints, viscosity constraints, and form constraints. This may speed up the process as the viscosity and form constraints may be less relevant, or have less of an impact, when the contour being deformed is still very small. Also alternatively, the selection of which constraints to apply may be set manually by an operator, or may be predetermined and triggered using criteria. 
       FIGS. 8 a  to 8 f    are an exemplary illustration of the deformation of a contour  806  inside of a structure  804  corresponding to a tibia. The femur  802  can also be seen in the image of a knee. In  FIG. 8 a   , the contour  806  is an initial contour formed by the four neighboring pixels surrounding a starting point, as determined using the method of  FIG. 4 . In  FIG. 8 b   , the contour  806  has been expanded one or more times from its size in  FIG. 8 a   . As can be seen, the shape defined by the contour  806  in  FIG. 8 b    is substantially symmetric, indicating that the constraints applied to each point along the contour have, up to this point, been substantially the same.  FIGS. 8 c  and 8 d    show that the constraints applied to the points along the contour  806  have begun to change, compared to  FIG. 8 b   , and be set locally as a function of various parameters at each point along the contour. With regards to  FIG. 8 c   , as the contour approaches an edge on its right side but not on its left side, deformation constraints are varied. Similarly in  FIG. 8 d   , only the bottom of the contour  806  is still far away from an edge and therefore, constraints for pixels along this part of the contour  806  vary from those elsewhere along the contour  806  at this stage of the deformation.  FIGS. 8 e  and 8 f    illustrate the contour  806  continuing to be expanded towards the bottom of the structure at a much higher rate than in other directions. The contour  806  appears to closely match the structure  804  along the left side, right side, and top edges of the structure  804  as it continues to expand towards the bottom. Even the notch on the top right corner of the structure  804  has been snuggly fitted with the contour  806  via the deformation process. 
     Referring now to  FIG. 9  in addition to  FIG. 1 , once the segmentation process  104  is completed for all images from a sequence, a points cloud may be rendered to represent the data obtained from the MRI. A meshing algorithm  106  may be applied to the rendered points cloud in order to convert the points cloud into a 3D mesh model. In particular, the step  106  of meshing images segmented along the first plane illustratively comprises obtaining the rendered points cloud at step  902 . Grid decimation  904  may then be performed to reduce the number of points to be processed. For this purpose, a bounding box may be defined around the points cloud and the bounding box may be further subdivided into a plurality of cells. The size of the cells may be determined from the data found in the parameter file discussed above with reference to  FIG. 1 . Grid decimation may then be applied so that each cell of the bounding box comprises only a single point from the points cloud. Indeed, for each point of the points cloud, the grid decimation technique may indeed determine which cell the point belongs to. This may be done using volumetric mapping to determine the volume occupied by the cell around the point in question. Once the cell to which the selected point belongs has been determined, the next step may be to determine whether the point is the first one in the cell. This assessment can be made by determining whether the selected point has a neighbor within the cell, the selected point being identified as the first one in the cell if it has no neighbors. If the selected point is the first one in the cell, the selected point is associated with the cell. Otherwise, if the cell already comprises another point, the selected point is discarded. In this manner, it can be ensured that each one of the cells delineated by the bounding box only comprises a single point. The number of points to be processed from the points cloud data can therefore be significantly reduced. It should be understood that other decimation techniques may also apply. 
     Once the grid decimation has been performed at step  904 , the normals of the points cloud are then computed at step  906 . Any suitable technique may be used, such as approximation techniques using Delaunay balls, plane fitting, or the like. The next step may then be to performing meshing  908  using any suitable technique, such as applying a power crust algorithm, a marching cube algorithm, a Poisson algorithm, or the like. For instance, screened Poisson surface reconstruction or parallel Poisson surface reconstruction may be used. Other meshing algorithms may apply. Also, mesh models having a suitable geometry, shape, and topology, e.g. triangular, hexagonal, tetrahedron, pyramidal, or the like, may be used. 
       FIG. 10  illustrates an exemplary rendered points cloud of the head of a tibia. Various known volume rendering techniques may be used, such as volume ray casting, splatting, shear warp, and texture mapping.  FIG. 11 a    illustrates a raw triangular mesh model. Smoothing and other refinements may be performed in order to produce the model of  FIG. 11 b   . The meshing algorithm may be a CAD-based approach or an image-based approach. The CAD-based approach uses an intermediary step of surface reconstruction which is then followed by a traditional CAD-based meshing algorithm. The image-based approach is a more direct way as it combines the geometric detection and mesh creation stages in one process and allows a straightforward generation of meshes from the segmented 3D data. A smooth and continuous 3D model as shown in  FIG. 11 b    may then be output at  108 , as per the general process illustrated in  FIG. 1 . Using the first plane meshed model(s) output at  108  for each structure being segmented, segmentation of images along the second plane, e.g. the coronal plane, may then be performed at step  110 . 
     Indeed, referring back to  FIG. 2 , the step  110  of segmenting second plane images illustratively comprises setting  201  the starting point for contour definition of a given structure in a set of images along the second plane. Step  110  is then illustratively followed by steps similar to those discussed above with reference to step  104  of segmenting first plane images. Referring now to  FIG. 12 , the step  201  of setting the starting point for contour definition along the second plane illustratively comprises obtaining  1002  the first plane meshed model(s) output at step  108  of  FIG. 1 . The next step  1004  may then be to compute slices of the first plane meshed model(s), the mesh slices being each computed at a position of a corresponding one of the second plane images acquired at step  102  of  FIG. 1 . The mesh slices may be computed using any suitable technique. For instance, an algorithm that computes the intersection between a cutting plane and each polygon, e.g. triangle, of the mesh model may be used. Other techniques may apply. 
     Once the mesh slices have been computed, the next step  1006  may be to compute a center point of each contour found in the computed mesh slice. Once a first contour center point has been computed at step  1006 , the step  1008  may be to determine whether another contour is present in the computed mesh slice. This is the case, for instance when the distal portion of the femur mesh model has been sliced, resulting in two (2) distinct contours each delineating a cross-section of the lateral and medial condyles. 
     If it is determined at step  1008  that another contour is present in the computed mesh slice, step  201  may flow back to the step  1006  of computing the center point of this other contour. Once it is determined at step  1008  that there are no other contours, i.e. the center points of all contours within the computed mesh slice have been computed, the next step  1010  may then be to determine whether there is another second plane image, e.g. another coronal image. If this is the case, step  201  may flow back to the step  1004  of computing a mesh slice for this other second plane image. Once it is determined at step  1010  that there are no other second plane images, the next step  1012  may then be to set the center point(s) as initial position(s), which may in turn be converted  1014  to initial contour(s). 
     Referring now to  FIG. 13 , the step  112  of merging first and second plane segmentations, e.g. sagittal and coronal segmentations, illustratively comprises obtaining at step  1102  third plane images from the imaging data acquired at step  102 . This may entail slicing a volume of data, e.g. a DICOM cube, acquired at step  102 . The first and second plane segmentations respectively performed at steps  104  and  110  of  FIG. 1  may then be obtained at step  1104 . The next step  1106  may then be to slice the first and second plane segmentations along the direction of a third plane, e.g. an axial direction, on the current position on a selected one of the third plane images. This results in first and second plane point lists being obtained at step  1108 . The point(s) within the neighborhood of each point in the first plane point list may then be computed  1110 . The next step  1112  may then be to assess whether there exists in the second plane point list points, which are at the same position as the neighbors of the first plane point. This would indicate a redundancy of information as the same data would be available in both the first plane segmentation and the second plane segmentation. For this purpose, accelerated tree search or any other suitable technique may be used. 
     If it is determined at step  1112  that one or more second plane points correspond to the first plane point neighbor(s), the identified second plane point(s) may then be removed from the second plane point list at step  1114 . In this manner, information from the second plane segmentation, which is redundant and therefore not needed to complement the information from the first plane segmentation, can be eliminated. If it is determined at step  1112  that no second plane points correspond to the first plane point neighbor(s) or once redundant second plane points have been removed, the next step  1116  may be to assess whether the first plane point list comprises another first plane point. If this is the case, the step  112  then flows back to step  1110 . Otherwise, the next step  1118  is to merge the first and second plane point lists. When implementing step  1118 , the data remaining in the second plane point list can then be used complement the data found in the first plane point list. 
     The next step  1120  may then be to determine whether segmentation is to be merged for another third plane image. If this is the case, step  112  may flow back to the step  1106  of slicing the first and second plane segmentations along the third plane on the current third plane image position. Once all third plane images have been processed, the third plane segmentation is obtained at step  1122 . 
     Referring to  FIG. 14 , there is illustrated a system for segmenting MRI images and generating 3D models, as described above. One or more server(s)  1200  are provided remotely and accessible via a network  1208 . The server  1200  is adapted to receive imaging data from an MRI apparatus  1210 , or from another computing device locally connected to the MRI apparatus, via any type of network  1208 , such as the Internet, the Public Switch Telephone Network (PSTN), a cellular network, or others known to those skilled in the art. 
     The server  1200  comprises, amongst other things, a plurality of applications  1206   a  . . .  1206   n  running on a processor  1204 , the processor being coupled to a memory  1202 . It should be understood that while the applications  1206   a  . . .  1206   n  presented herein are illustrated and described as separate entities, they may be combined or separated in a variety of ways. The processor  1204  is illustratively represented as a single processor but may correspond to a multi-core processor or a plurality of processors operating in parallel. 
     One or more databases (not shown) may be integrated directly into memory  1202  or may be provided separately therefrom and remotely from the server  1200 . In the case of a remote access to the databases, access may occur via any type of network  1208 , as indicated above. The various databases described herein may be provided as collections of data or information organized for rapid search and retrieval by a computer. They are structured to facilitate storage, retrieval, modification, and deletion of data in conjunction with various data-processing operations. They may consist of a file or sets of files that can be broken down into records, each of which consists of one or more fields. Database information may be retrieved through queries using keywords and sorting commands, in order to rapidly search, rearrange, group, and select the field. The databases may be any organization of data on a data storage medium, such as one or more servers. 
     In one embodiment, the databases are secure web servers and Hypertext Transport Protocol Secure (HTTPS) capable of supporting Transport Layer Security (TLS), which is a protocol used for access to the data. Communications to and from the secure web servers may be secured using Secure Sockets Layer (SSL). An SSL session may be started by sending a request to the Web server with an HTTPS prefix in the URL, which causes port number “443” to be placed into the packets. Port “443” is the number assigned to the SSL application on the server. Identity verification of a user may be performed using usernames and passwords for all users. Various levels of access rights may be provided to multiple levels of users. 
     Alternatively, any known communication protocols that enable devices within a computer network to exchange information may be used. Examples of protocols are as follows: IP (Internet Protocol), UDP (User Datagram Protocol), TCP (Transmission Control Protocol), DHCP (Dynamic Host Configuration Protocol), HTTP (Hypertext Transfer Protocol), FTP (File Transfer Protocol), Telnet (Telnet Remote Protocol), SSH (Secure Shell Remote Protocol), POP3 (Post Office Protocol 3), SMTP (Simple Mail Transfer Protocol), IMAP (Internet Message Access Protocol), SOAP (Simple Object Access Protocol), PPP (Point-to-Point Protocol), RFB (Remote Frame buffer) Protocol. 
     The memory  1202  accessible by the processor  1204  receives and stores data. The memory  1202  may be a main memory, such as a high speed Random Access Memory (RAM), or an auxiliary storage unit, such as a hard disk, flash memory, or a magnetic tape drive. The memory  1202  may be any other type of memory, such as a Read-Only Memory (ROM), Erasable Programmable Read-Only Memory (EPROM), or optical storage media such as a videodisc and a compact disc. 
     The processor  1204  may access the memory  1202  to retrieve data. The processor  1204  may be any device that can perform operations on data. Examples are a central processing unit (CPU), a front-end processor, a microprocessor, a graphics processing unit (GPU/VPU), a physics processing unit (PPU), a digital signal processor, and a network processor. The applications  1206   a  . . .  1206   n  are coupled to the processor  1204  and configured to perform various tasks as explained below in more detail. An output may be transmitted to an output device (not shown) or to another computing device via the network  1208 . 
       FIG. 15  illustrates an exemplary application  1206   a  running on the processor  1204 . The application  1206   a  comprises at least segmenting module  1302 , a meshing module  1304 , and a segmentation merging module  1306 . These modules interact together in order to provide the 3D models from the segmented data. The acquired data is received by the segmenting module  1302  and processed in accordance with the flowcharts of  FIGS. 2 to 7, 9, 12, and 13  in order to generate segmented data. First plane segmented data is transmitted to the meshing module  1304  for further processing. The meshing algorithms are applied by the meshing module  1304  to produce a smooth and continuous 3D model, as illustrated in  FIG. 10 b   . The meshed models are then output by the meshing module  1304  to the segmenting module  1302 , which generates therefrom a second plane segmented data, as discussed above with reference to  FIG. 1 ,  FIG. 2 , and  FIG. 12 . The segmenting module  1302  then outputs the first and second plane segmented data to the segmentation merging module  1306 , which generates from the received data third plane segmented data, as discussed above with reference to  FIG. 13 . 
       FIG. 16  illustrates an exemplary embodiment of the segmenting module  1302 , whereby the acquired data and/or the meshed model data is first run through an initial position module  1402  in accordance with the steps of  FIGS. 3 a , 3 b   ,  4 , and  12 . Position data is exchanged between the initial position module  1402  and a contour definition module  1404 , whereby the steps of  FIGS. 5 to 7  are applied to the acquired data. As per the illustrated embodiment, the acquired data may be provided directly to both the initial position module  1402  and the contour definition module  1404  in order to facilitate parallel processing and simplify the data exchanged between the initial position module  1402  and the contour definition module  1402 . Segmented data is output from the contour definition module  1404 . 
       FIG. 17  is an exemplary embodiment of the contour definition module  1404 . A contour deformation module  1508  interacts with a deformation constraints module  1502 , a viscosity constraints module  1504 , and a form constraints module  1506 . Each one of the deformation constraints module  1502 , the viscosity constraints module  1504 , and the form constraints module  1506  may access the memory  1202  in order to retrieve previously stored data, such as initial contours, edge lists, contour Normals, vector fields, threshold information, etc. For example, the deformation constraints module  1502  may generate a dynamic matrix for continuity and curvature based on measured distances between contour points and a nearest edge. The dynamic matrix may be updated upon receipt of new information from the contour deformation module  1508  or by accessing the memory  1202 . In another example, the viscosity constraints module  1504  uses stored vector fields to evaluate field magnitudes at each point along a contour, and then selectively applies a force at each point along the contour as a function of the field magnitudes. Various embodiments for implementing the steps of  FIGS. 6 and 7  using the contour definition module  1404  of  FIG. 17  will be readily understood by those skilled in the art. 
     While illustrated in the block diagrams as groups of discrete components communicating with each other via distinct data signal connections, it will be understood by those skilled in the art that the present embodiments are provided by a combination of hardware and software components, with some components being implemented by a given function or operation of a hardware or software system, and many of the data paths illustrated being implemented by data communication within a computer application or operating system. The structure illustrated is thus provided for efficiency of teaching the present embodiment. 
     It should be noted that the present invention can be carried out as a method, can be embodied in a system, and/or on a computer readable medium. The embodiments of the invention described above are intended to be exemplary only. The scope of the invention is therefore intended to be limited solely by the scope of the appended claims.