FIELD OF THE INVENTION

The present disclosure relates to systems and methods for manufacturing customized surgical devices, more specifically, the present disclosure relates to automated systems and methods of arthroplasty cutting guides, systems and methods for image segmentation in generating computer models of knee joint.

INTRODUCTION TO THE INVENTION

The success of TKA operations depends on the restoration of correct knee alignment. It has been demonstrated that the key to the stability of the knee joint is the restoration of the mechanical axis with balanced flexion and extension gaps. Traditionally, intramedullary and extramedullary jigs were used to assist in orientation of the femoral and tibial components. Computer assisted surgery has been developed to assist surgeons in proper positioning and orientation of the components. However, surgical navigation systems are not widely used in hospitals. Main arguments against surgical navigation systems are the high costs and extra time spent in the operating room, in addition to a steep learning curve.

The desire for a simple and accurate system as a substitute motivated the orthopaedic industry to develop a technique involving the use of patient specific cutting jigs. Magnetic resonance images (MRIs) of the patient's hip, knee, and ankle are used to generate a model of specific patient anatomy. From these images, software is used to create virtual 3D models of the femur and tibia and orient these bones in space. Implant size is determined with the software and virtual bone resections are mapped to perform implant positioning and alignment using disposable custom guides that fit on the patient's bone to determine pin placement for the standard resection instrumentation of the implant manufacturer.

However, a number of drawbacks are associated with MRI including scanning cost, increased scanning time, geometrical distortion, and the need to standardize the scanning protocol among different MRI vendors. Recently Computerized Tomography CT is being used as well but the radiation associated with the procedure may not be favorable for some patients. An alternative approach to MRI or CT is to use the statistical anatomical shape analysis methodology to model the hylene cartilage accurately using x-rays and/or ultrasound.

Statistical anatomical shape analysis has rapidly established itself as an invaluable tool in the design process for orthopaedic implants and patient specific solutions. Thus, the intelligent Cartilage System (iCS) was conceived, with the goal of putting statistical anatomical shape analysis to accurately model cartilage thickness and contours to produce custom cutting guides (or Jigs). The guides are designed to place the cuts such that the knee is returned to its normal anatomical state before the degeneration of cartilage. The iCS system is built on the foundational merger of statistics and three-dimensional bone modeling.

The iCS platform represents a cohesive software system encompassing multidimensional medical imaging, computer aided design (CAD) and computer graphics features integrated for designing patient specific cutting jigs. The promise of coordinated interaction, efficiency, and mass customization allows iCS to address the complexities of increased patients' volume with accuracy and speed. A smart database composed of bone and cartilage atlases provide technology utilized within the customized module for custom jig generation. By reducing turn-around time in the current custom jig process, the iCS platform minimizes the risk of bottlenecks.

The database stores patient information following HIPPA regulations. Data from different imaging modalities will be attached with each patient including DICOM from MRI/CT, X-ray images, and ultrasound. Reconstructed bones and cartilage are stored in the database. Virtual templating data including calculated landmarks, axes, implant sizing, and placement information are also stored in the database. This component implements both relational and XML schema to provide a powerful tool for data storage and manipulation.

Bone Cartilage Reconstruction Subsystem: This module involves reconstruction of both bones and soft tissues either by segmentation from MRI, CT, PET or Ultrasound or direct reconstruction from RF signals as in microwave imaging and US or three dimensional bone reconstruction from 2D X-ray images, flow chart outlining this subsystem can be found inFIG. 4.

Segmentation of medical imaging can be roughly divided into two categories; Structural and Statistical. Structural approaches are based on spatial properties of the image pixels such as edges and regions. Statistical approaches rely on the probability distribution of intensity values in labeling the image regions. Image intensities and their corresponding class labels are considered to be random variables. Therefore, the statistical approaches tend to solve the problem of evaluating the class label, given the image intensity value. Previous segmentation attempts have combined one or more of these methods trying to overcome the limitations of individual methods.

Most of the segmentation using the edge oriented approach is semi-auto segmentation, where it is used to extract contours, optimizing contour and propagating it to neighbor slices. Image preprocessing and gradient operators can be used to enhance typical region growing methods. If an organ is known to be present in a certain area of an image by placement of a seed (either manually or automatically), the region can expand until it reaches the contrast boundaries of the organ. Parameters can vary to change the momentum of the growing, making the algorithm more or less sensitive to small changes in grayscale.

Region-Oriented Segmentation with Knowledge-Based Labeling is a pixel classification technique based on the homogeneity of some features of the object. Various approaches such as knowledge based with uncertainty reasoning, static domain knowledge from high order features extraction, fuzzy logic, long term and short term memory modeling, as well as unsupervised clustering have been used in this area. They yield very good result with MRI images because of the high contrast between soft tissues. The global interpretation error of the brain is 3.1% and the interpretations error for sub-regions of the brain is 9%.

The watershed approach has been used in combination with various other segmentation methods in hopes of improving accuracy. The watershed algorithm is simply explained with a 3D plot of the grayscale intensities in an image; the “water” fills the valleys of the image until two valleys meet. This provides connectivity information about different areas of the image by relying on grayscale values. Pre-processing with edge enhancement such as Sobel filters and texture analysis can aid in detection of different organs.

Clustering algorithms are non-supervised algorithms that iterate between segmenting the image and characterizing the properties of each class until well defined image clusters are formed. Examples for such algorithms are the K-means algorithm, the fuzzy c-means algorithm, the expectation maximization (EM) algorithm and Self-Organized Neural Networks. The K-means clustering algorithm clusters data by iteratively computing a mean intensity for each class and segmenting the image by classifying each pixel in the class with the closest mean. It was used to segment the brain. The number of classes was assumed to be three, representing cerebrospinal fluid, gray matter, and white matter.

The fuzzy c-means algorithm generalizes the K-means algorithm, allowing for soft segmentations based on fuzzy set theory. The EM algorithm applies the same clustering principles with the assumption that the data follows Gaussian mixture model. It iterates between computing the posterior probabilities and computing maximum likelihood estimates of the means, covariances, and mixing coefficients of the model. Since algorithms do not directly incorporate spatial modeling they are sensitive to noise and intensity inhomogeneities. This can be overcome by using Markov Random Field modeling.

Morphological Operators like binary morphology has been used in several segmentation systems, the basic idea in morphology is to convolve an image with a given mask (known as the structuring element), and to binarize the result of the convolution using a given function. Examples are: Erosion, Dilation, Opening and Closing.

Statistical approaches like thresholding label regions of the image by using its intensity value histogram. A maximum and minimum threshold defines the region on interest depending on the knowledge base. For example, in CT, rough segmentation of organs can be achieved by thresholding the image according to the Hounsfield unit ranges for organs of interest. It has been applied in digital mammography, in which two classes of tissue are typically present; healthy and tumorous. Limitations of depending solely on such a method are due to the usual overlap of organ intensities' intervals, intensity inhomogeneities, and sensitivity to noise and image artifacts. Since thresholding does not take into account the spatial characteristics of the image, any artifact that distorts the image histogram can eventually affect the segmentation results. Nevertheless, thresholding remains to be an initial step in many segmentation algorithms. Variations on classical thresholding have been proposed for medical image segmentation that incorporate information based on local intensities and connectivity.

Deformable template matching is performed with 2D deformable contours, which involves detection of the contour, tracking, matching and optimizing the error of the match. In segmentation, 2D deformable contour is applied with an atlas, which permits mapping between the image data and the atlas by constrained minimization of predefined data. A 3D deformable surface is also realized by tracking contour changes between slides. Bayesian statistics is commonly used for determining the model priors or likelihood in order to separate the organ of interest with its neighboring organs. In general, the approach produces good results for small and local shape changes. It is also suitable for large and global misalignments or deformations. A major improvement came from using principle geodesic analysis, which determines the mean shape of an object and the principle mode of variation, and an m-rep model, which the object is represented as a set of connected meshes of medial atoms. This method yields excellent results for automatic segmentation of kidneys with 0.12 cm mean surface separation and 88.8% volume overlay as compare to manual segmentation.

Knowledge-Based Approach with Blackboard Architecture system, in general, is an area of shared memory that contains a problem to be solved and a number of different processes. The blackboard is continually constructed and updated along the reasoning process. The labeling of the anatomical structures of the image data is done by matching the structure in the image to the corresponding objects in the models. The data from the image and the model are transformed into a common, parametric feature space for comparisons. The low and high level features extracted are written to the blackboard and compare with the model. The result is also written onto the blackboard to guide further matching. Long-term memory on the descriptions and relationships of the object can be written into the knowledge base.

Four Dimensional Markov Random Fields (MRF), present a method using a 4D probabilistic atlases to segment moving targets such as the heart. The atlases predict the time and space variance based on a priori information; the algorithm also incorporates spatial and temporal contextual information using 4D Markov Random Fields (MRF). Global connectivity filters finish the segmentation, using the largest connected structure as a starting point. The results presented were very favorable for segmentation of the heart and are not limited to MRI images. The results for left ventricle (LV) 96%, myocardium 92% and right ventricle (RV) 92% as compared to manually segmented models.

Our system utilizes the information from any three dimensional imaging modality to extract gradients information combined with statistical atlases to extract bone boundaries, and cartilage interfaces. Bellow detailed description of each these bone reconstruction modalities.

