Contouring of the organs at risk is a vital part of routine radiation therapy planning. For the head and neck (H and N) region, this is more challenging due to the complexity of anatomy, the presence of streak artifacts, and the variations of object appearance. In this paper, we describe the latest advances in our Automatic Anatomy Recognition (AAR) approach, which aims to automatically contour multiple objects in the head and neck region on planning CT images. Our method has three major steps: model building, object recognition, and object delineation. First, the better-quality images from our cohort of H and N CT studies are used to build fuzzy models and find the optimal hierarchy for arranging objects based on the relationship between objects. Then, the object recognition step exploits the rich prior anatomic information encoded in the hierarchy to derive the location and pose for each object, which leads to generalizable and robust methods and mitigation of object localization challenges. Finally, the delineation algorithms employ local features to contour the boundary based on object recognition results. We make several improvements within the AAR framework, including finding recognition-error-driven optimal hierarchy, modeling boundary relationships, combining texture and intensity, and evaluating object quality. Experiments were conducted on the largest ensemble of clinical data sets reported to date, including 216 planning CT studies and over 2,600 object samples. The preliminary results show that on data sets with minimal (<4 slices) streak artifacts and other deviations, overall recognition accuracy reaches 2 voxels, with overall delineation Dice coefficient close to 0.8 and Hausdorff Distance within 1 voxel.
Algorithms for image segmentation (including object recognition and delineation) are influenced by the quality of object appearance in the image and overall image quality. However, the issue of how to perform segmentation evaluation as a function of these quality factors has not been addressed in the literature. In this paper, we present a solution to this problem. We devised a set of key quality criteria that influence segmentation (global and regional): posture deviations, image noise, beam hardening artifacts (streak artifacts), shape distortion, presence of pathology, object intensity deviation, and object contrast. A trained reader assigned a grade to each object for each criterion in each study. We developed algorithms based on logical predicates for determining a 1 to 10 numeric quality score for each object and each image from reader-assigned quality grades. We analyzed these object and image quality scores (OQS and IQS, respectively) in our data cohort by gender and age. We performed recognition and delineation of all objects using recent adaptations [8, 9] of our Automatic Anatomy Recognition (AAR) framework [6] and analyzed the accuracy of recognition and delineation of each object. We illustrate our method on 216 head & neck and 211 thoracic cancer computed tomography (CT) studies.
Segmentation of organs at risk (OARs) is a key step during the radiation therapy (RT) treatment planning process. Automatic anatomy recognition (AAR) is a recently developed body-wide multiple object segmentation approach, where segmentation is designed as two dichotomous steps: object recognition (or localization) and object delineation. Recognition is the high-level process of determining the whereabouts of an object, and delineation is the meticulous lowlevel process of precisely indicating the space occupied by an object. This study focuses on recognition.
The purpose of this paper is to introduce new features of the AAR-recognition approach (abbreviated as AAR-R from now on) of combining texture and intensity information into the recognition procedure, using the optimal spanning tree to achieve the optimal hierarchy for recognition to minimize recognition errors, and to illustrate recognition performance by using large-scale testing computed tomography (CT) data sets. The data sets pertain to 216 non-serial (planning) and 82 serial (re-planning) studies of head and neck (H&N) cancer patients undergoing radiation therapy, involving a total of ~2600 object samples. Texture property “maximum probability of occurrence” derived from the co-occurrence matrix was determined to be the best property and is utilized in conjunction with intensity properties in AAR-R. An optimal spanning tree is found in the complete graph whose nodes are individual objects, and then the tree is used as the hierarchy in recognition. Texture information combined with intensity can significantly reduce location error for glandrelated objects (parotid and submandibular glands). We also report recognition results by considering image quality, which is a novel concept. AAR-R with new features achieves a location error of less than 4 mm (~1.5 voxels in our studies) for good quality images for both serial and non-serial studies.
Currently, there are many papers that have been published on the detection and segmentation of lymph nodes from medical images. However, it is still a challenging problem owing to low contrast with surrounding soft tissues and the variations of lymph node size and shape on computed tomography (CT) images. This is particularly very difficult on low-dose CT of PET/CT acquisitions. In this study, we utilize our previous automatic anatomy recognition (AAR) framework to recognize the thoracic-lymph node stations defined by the International Association for the Study of Lung Cancer (IASLC) lymph node map. The lymph node stations themselves are viewed as anatomic objects and are localized by using a one-shot method in the AAR framework. Two strategies have been taken in this paper for integration into AAR framework. The first is to combine some lymph node stations into composite lymph node stations according to their geometrical nearness. The other is to find the optimal parent (organ or union of organs) as an anchor for each lymph node station based on the recognition error and thereby find an overall optimal hierarchy to arrange anchor organs and lymph node stations. Based on 28 contrast-enhanced thoracic CT image data sets for model building, 12 independent data sets for testing, our results show that thoracic lymph node stations can be localized within 2-3 voxels compared to the ground truth.
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