Head and neck lymph node region delineation with image registration
© Teng et al; licensee BioMed Central Ltd. 2010
Received: 13 May 2009
Accepted: 22 June 2010
Published: 22 June 2010
The success of radiation therapy depends critically on accurately delineating the target volume, which is the region of known or suspected disease in a patient. Methods that can compute a contour set defining a target volume on a set of patient images will contribute greatly to the success of radiation therapy and dramatically reduce the workload of radiation oncologists, who currently draw the target by hand on the images using simple computer drawing tools. The most challenging part of this process is to estimate where there is microscopic spread of disease.
Given a set of reference CT images with "gold standard" lymph node regions drawn by the experts, we are proposing an image registration based method that could automatically contour the cervical lymph code levels for patients receiving radiation therapy. We are also proposing a method that could help us identify the reference models which could potentially produce the best results.
The computer generated lymph node regions are evaluated quantitatively and qualitatively.
Although not conforming to clinical criteria, the results suggest the technique has promise.
Malignant tumors in the head and neck represent a great epidemiological problem in western countries. Head and neck cancer accounts for approximately 3% of all cancer cases reported in the United State, or roughly 50,000 cases per year . Due to the tumor position, the risk of developing lymph node metastases in the neck region is very high. Radiation therapy is used as part of the treatment in a majority of the cases. Therefore a fast and effective system for creating a conformal radiation treatment for enlarged (i.e. potentially malignant) lymph nodes is essential.
Creating the 3D CTV is a critical part of the 3D radiation treatment and Intensity-Modulated Radiation Therapy (IMRT) as the success of radiotherapy depends on the accuracy of the CTV. A conformal IMRT plan with accurately drawn CTV can avoid critical anatomic structures and maximize radiation dosage. As 3D conformal radiotherapy and IMRT become the state of the art, the process of CTV delineation is more important than ever. This process currently also requires radiation oncologists to manually draw the 2D target contours on axial CT slices. It is tedious, time consuming and can be the bottle neck to make IMRT available to more patients. As imaging based cervical lymph node region classification is developed, it is possible to design a system that can identify critical anatomic structures and contour CTV by segmenting patients' CT images with little or no user interaction. Software tools that automate the segmentation of critical structures and contouring of target volume is crucial to the success of implementing a fast and effective radiation treatment planning system as it can dramatically decrease the planning effort for radiation oncologists and increase the availability of IMRT to more patients. The objective of this study is to create a prototype system which is capable of generating a patient's head and neck CTV contours from his CT scan. This paper summarizes our previous work [5–8] and presents a complete system with more comprehensive results.
Imaging-based lymph node regions
The neck has an extensive lymphatic network . In fact, more than one third of the body's total number of lymph nodes resides in the extracranial head and neck. Cervical lymph nodes are divided into regions or 'levels' that are described by their anatomic location . Although this traditional classification was decided using surgical landmarks, translation into an imaging-based nodal classification is feasible.
Automatic segmentation of cervical lymph nodes remains to be an open problem, researchers [11, 12] are actively working on techniques to segment the lymph nodes for diagnosis or surgery planning. However, in the context of radiation therapy planning, the exact contours of lymph nodes are not as important as the lymph node regions including the surrounding tissue which make up the CTV. Studies have been conducted to create an imaging-based classification for the lymph node levels of the neck that can be accepted by clinicians and easily used by radiologists [4, 13–17]. Anatomic landmarks were chosen to create a consistent nodal classification similar to the clinically-based classifications. Radiologists must be able to identify the pertinent anatomic landmarks such as the bottom of the hyoid bone, the back edge of the submandibular gland, and the back edge of the sternocleidomastoid muscle. The Radiation Therapy Oncology Group (RTOG)  has also published guidelines for CT-based delineation of lymph node levels in the neck and the anatomic boundaries for delineation.
Automatic delineation of lymph node region can reduce physicians' manual CTV contouring time even though the results are not sufficiently accurate for clinical use directly [19, 20]. Atlas-based segmentation is used in most of the state of the art research [17, 21, 22] and commercial tools [23, 24] for automatic delineation of lymph node levels in head and neck CT. These methods tend to yield better results when the atlas is more anatomically similar with the target subjects. The method and database (CT images) used to construct an unbiased atlas is critical to the success of the segmentation [25, 26]. However, the high anatomical variability in post-operative head and neck CT images makes it very difficult to construct a mean image and atlas that works well for all patients. We proposed an alternative approach which uses a collection of CT images with contoured CTV from previously treated patients as reference models , and a method to identify reference subjects whose anatomic structures share similar properties or features for a given target . Using previously treated patients or canonical models with the most similar head and neck anatomy as references, an image registration process can segment lymph node regions more accurately for a target patient based on known contours in the reference models.
Recent studies evaluated some of the state of the art atlas-based segmentation tools listed above by comparing the automatically delineated head and neck lymph node region contours and volumes against the ones drawn by physicians [27–29]. In addition to qualitative assessment by physicians, statistics measures such as sensitivity and specificity or Dice similarity coefficient were commonly used as quantitative assessment. We also proposed an alternative quantitative evaluation using Hausdorff surface distance measure which maybe more clinically relevant than the statistical metrics .
Given the set of post-op head and neck cancer patients, a series of 2D contours were manually delineated for each of the lymph node levels on axial CT images; which build up to 3D volumes. Using an image registration technique, these expert drawn lymph node regions are used as reference models and templates to project the lymph node regions in another target image which are compared to the expert drawn contours in the target image, i.e. the "gold standard" or "ground truth". Instead of the atlas-based approach or choosing one patient as the reference model, we will determine criteria for choosing one or more similar reference models which can produce optimal results. Traditional 3D shape retrieval systems [30–32] mostly experiment with artificial models and focus mainly on classifying 3D models of very different shapes. While these experimental systems can match models of the same classes to a certain degree of success, they usually fall short of distinguishing the finer details of objects within a class. Using 3D medical images to find similarity among a known set of patients is becoming a research subject of interest in many medical domains. Ruiz et al.  use a shape-based similarity measure to find similar craniosynostosis patients for intervention planning. We developed a method to find similar head and neck cancer patients for radiation planning. The similarity of head and neck anatomy between patients is based not only on shape features of structures, such as outer body volume, mandible, and hyoid, but also on their relative locations. These types of medical-image-based problems are very domain specific, and are not solved by the traditional shape-based retrieval system.
Recent reviews of 3D shape matching techniques were done by Iyer  and Tangelder . A majority of the 3D shape matching systems use feature-based methods, which compare geometric and topological properties of 3D shapes. Methods using features or distributions work reasonably well in classifying objects of different shapes, but they do not discriminate between objects of the same class such as the head and neck anatomy of different patients. The matching process is usually done by computing a distance between feature vectors representing the different objects. Most systems do not give many details on the distance measurements or their comparison methods, although they usually imply a Euclidian vector space model and use either a simple (weighted) Euclidean distance or a city-block (L 1 Minkowski) distance.
Retrieval of similar reference models,
Given a reference model and a target patient's CT data set, we can use image registration methods to align the sets of CT images. Image registration is commonly used in medical imaging applications. It is essentially a process of finding a geometric transformation g between two sets of images, which maps a point x in one image-based coordinate system to g(x) in the other. By assuming the head and neck anatomy has similar characteristics between a specific target patient and a reference subject, we can use image registration methods to transform a region from the reference image set to the target image set.
The first three parameters γ, θ, ϕ are the roll-pitch-yaw Euler angles of R. The translation vector T is defined by [t x , t y , t z ]T. T and R together define the rigid body transformation.
where q x , q y , and q z are the dimensions of the reference image.
By displacing the control points, intermediate deformation values are computed by cubic spline interpolation between them.
Because contract enhancement is often used in head and neck CT scans, image intensity range and distribution may vary in different data sets. A mutual information based registration method such as Mattes' can work with images in different range. However, the high anatomical variability in cancer patients' head and neck CT images particularly contributed by the surgical resections, pure voxel intensity based registration such as Mattes' method described in previous section does not always produce satisfactory results. A new method  is developed which integrates landmark based information with intensity scheme; hence CT data set need to be preprocessed to extract landmark information.
Segmentation and landmark correspondence
Fully automatic segmentation in the neck region is particularly difficult, because many soft tissue anatomic entities are small in size and similar in density. Furthermore, they can be directly adjacent to each other or only divided by fascial layers that are not visible in CT images. The relative locations between anatomic entities can vary in different axial locations. Little work has been done specifically for the neck images; few exceptions include the work of Krugar et al.  who implemented a semi-automatic system to segment neck CT images for pre-operative planning of neck dissections; and the work of Cordes et al.  who developed NeckVision system for neck dissection planning. We implemented an automatic segmentation method  designed to locate anatomic structures in the neck that are relevant to the lymph node region boundaries including cervical spine, mandible, hyoid, jugular veins, and carotid arteries. This method is motivated by a knowledge-based technique  using consists of constraint-based dynamic thresholding, negative shape constraints to rule out infeasible segmentation, and progressive landmarking that takes advantage of the different degrees of certainty of successful identification of each structure.
The drawback of this 2D thresholding approach is the difficulty in determining the optimal threshold, especially in the head and neck region where many structures of similar density are crowded in a tight space. Our proposed method eliminates the need of finding the optimal threshold by combining the 2D thresholding with a 3D active contour procedure (by ITK, www.itk.org). A sub-optimal threshold that only produces partial structure on some axial slices can grow into a 3D volume through 3D active contouring. The 2D regions produced by the knowledge-based dynamic thresholding method can be used as the initial region or "seed" to eliminate the need of user input. In the context of finding relevant landmarks, it is not necessary to fully segment certain structures such as the blood vessels and their branches as we are only interested in sections lateral to the hyoid. This lax requirement circumvents the need of perfect segmentation and optimal active contouring parameters. These selected structures usually have clear contour allowing successful segmentation within the section of axial slices of interests.
where C is the function that maps points on surface A to matching points on surface B, α and β are weight parameters, E sim is the similarity term which measures how closely C matches points on A to points on B, E str is the structural term that minimize the distortion of surface A, and E pri is the "prior information" term which ensures C represent a plausible deformation. Initial values of α and β are set to 0.001 and 0.0001 respectively.
Image registration with landmark correspondence
where 0 ≤ l ≤ ρ x , 0 ≤ m ≤ ρ y , 0 ≤ n ≤ ρ z , and the corresponding deformation values D(λ j ) are either initially set to zero or calculated from the deformation coefficients δ j of the previous iteration at a lower resolution of control points as in equation (5).
where υ k is the closest landmark point to λ j in the reference image set, and ζ k is the deformation at υ k obtained from the surface correspondence in equation (7). A new set of deformation coefficients δ is then set to the spline coefficients of the new grid of deformation values D'(λ). While the new D' might not be initially smooth, the following mutual information registration will mitigate the side effect. Finally the transformation parameter vector μ is input to the optimizer for alignment.
Similarity measurement based on anatomical structure geometry
volume and extents of the overall head and neck region,
surface meshes of the mandible and outer body contour,
3D centroid difference vector between mandible and hyoid,
2D centroid difference vectors between hyoid and jugular veins, and between hyoid and spinal cord on the axial slice at the centroid of the hyoid,
normalized centroid locations of the hyoid and the mandible within the region.
d h is the Hausdorff distance defined in equation (12), and T is the ICP registration transformation matrix. The weight parameters range from 10 to 0.1 from heavy to light in the order of: 1) hyoid locations (normalized and relative to other structures), 2) mandible distance (between two models), 3) vertical distances between skull base, mandible and, hyoid, 4) head and neck outer contour and volume, and so on.
Results and discussion
CT images are acquired from the radiation therapy treatment planning system. Head and neck cancer patients who did not have large scale surgical resection are the primary candidates. All the images used in the experiments are CT scans performed at the University of Washington Medical Center using a General Electric CT scanner. Head and neck CT images were selected in which all of the slices are 512 × 512 pixels in axial dimension; the distance between slices varies between 1.25 mm and 3.75 mm for each image set. Selected lymph node regions are drawn as 2D contours on axial CT slices of twenty subjects chosen by resident physicians in University of Washington Radiation Oncology Department. These lymph node regions are used as "ground truth" or "gold standard" as we evaluate the computer generated lymph node regions. A simple automated segmentation pre-process removes the bed and immobilization devices on each CT images. The experiment was conducted on personal computers with Pentium 4 processors and 2 GB RAM.
We ran the image registration experiment with the twenty sets of CT images chosen by the oncologists, each of which is used as a reference and a target data set to align with every other image set. The maximum resolution of the deformation control points as defined in Eq. (3) is [15, 15, 11], or 2475 control points. The pairing results in 20 × (20-1) = 380 total registration tests. We used the results of the Mattes method as the baseline to compare with the results of the new method.
Success rate comparison between Mattes and proposed method.
Successful registration cases
Success rate (%)
Proposed method using landmark correspondence
Time of convergence comparison between Mattes and proposed method.
Average time of convergence
Standard deviation time of convergence
Proposed method using landmark correspondence
Example of volume overlap analysis with Dice similarity index (DSC).
D H (TS R , S T , L) for all S R , S T . Hausdorff distance (in cm) between transformed reference mesh and target mesh for nodal levels (L) 1B and 2.
Mean distance (in cm) between transformed reference mesh and target mesh for nodal level 1B and 2.
There is currently no quantitative standard defining clinical acceptability for automatically delineated target contours, whether in statistical error or maximum surface distance error. However, as the results for two sample lymph node region shown in Tables 4 and 5, the average distances (error) between the generated CTV and ground truth are under 1 cm for the cases tested; the Hausdorff distances are still in the 3 cm range. Methods are being investigated to reduce the maximum error as part of the future work for this continuing project. One of which is to port the registration module to a supercomputer platform and increase to resolution of the control points during the deformation process. Another is to merge the results of projected CTV from multiple similar reference models and possibly alleviate regions that contribute the worst Hausdorff distance.
While the proposed method improves the overall results, it does not decrease the Hausdorff or mean distance in every case. One reason is that some of the "ground truth" contours deviate from the image-based classification because physicians were sometimes influenced by their clinical judgment as they outline the contours . We also observed that for cases which already produce small Hausdorff or mean distance, the improvement or regression tends to be negligible. As the collection of reference data sets grow, it becomes critically important to locate the candidates which can potentially produce the best results based on the similarity measurement.
Ranking R I ranks by Hausdorff distance D H between projected lymph node region and corresponding expert drawn region of target subject. The smallest value has the highest rank. This represents the ranking of similarity according to the image registration results, assuming that the more similar reference image aligns better with the target image. Note that there may be different rankings for each lymph node region as the Hausdorff distance results vary. These rankings are used as the baseline.
Ranking R F ranks by distance measure in the feature vector space D F in Equation (13). The smallest value has the highest rank. This represents the ranking of similarity according to the geometrical features.
Probabilities of the most similar subject based on geometric features matching subjects that align best with target.
P(R I (x, S T ) = R F (1, S T ) | R F (1, S T )), where R F (1, S T ) is the most geometrically similar reference subject
Compare image registration results based on most similar reference subject and results based on all test cases.
D H (TR F (1, S T ), S T , n) for all S T and n, based on most similar reference subjects of each target subject.
D H (TS R , S T , n) for all S R , S T and n, based on all combinations of reference and target subjects.
Correlation coefficients of D F and D H for all the target subjects S T .
Correlation_coefficient(D H , D F ) for all S T .
This software system is the first of its kind that attempts to automate the process of identifying Clinical Target Volume for head and neck cancer radiation treatment by projecting lymph node regions using inter-subject image registration techniques. We developed a image registration technique using landmark correspondences in conjunction with a voxel based mutual information method, along with a patient similarity measurement using the 3D geometrical relationship between anatomic structures in addition to 3D shape descriptors of the structures. The image registration technique provides a way to potentially automate the lymph node region contouring process for radiation therapy planning. While the alignment of the projected lymph node contours on the target image are close enough to suggest the technique has promise, the results do not conform to clinical criteria. However, this system could drastically reduce the time needed for a physician to draw a fully-detailed target volume as an intermediate step that suggests an initial target volume upon which the physician can adjust and refine before it is used for therapy.
While the new image registration technique improves the overall result of the target volume projection, the results discussed in previous section shows that it is perhaps more important to identify the reference models which can potentially produce the best alignment. As the similarity measurement is validated by the target volume projection, twenty data sets with "ground truth" information is not quite enough to provide a variety of subjects with different anatomic features. We will expand the test data set to refine and improve the method, while continuing to investigate other potential features that can be incorporated in the similarity measure for better results, including more anatomic structures and their relationships. In addition to using one reference model to project the CTV for a test subject as in the current method, we will investigate the possibility of using multiple similar reference models and merge their CTV projects to further reduce the maximum error.
These software tools will need to be evaluated at a clinical environment for head and neck cancer patients who undergo 3D conformal radiation therapy where the computer generated target contours can be compared to the expert drawn contours of more target subjects. The method can also be generalized for treatment of other types of cancer. Furthermore, this work can potentially be integrated with other research work such as predictions of the regional lymphatic involvement of head and neck squamous cell carcinoma based on primary tumor location and T-stage . By using the lymphatic prediction, we can delineate proper target volumes for given primary tumor location and T-stage.
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