3D reconstruction of the human rib cage from 2D projection images using a statistical shape model.

PURPOSE: This paper describes an approach for the three-dimensional (3D) shape and pose reconstruction of the human rib cage from few segmented two-dimensional (2D) projection images. Our work is aimed at supporting temporal subtraction techniques of subsequently acquired radiographs by establishing a method for the assessment of pose differences in sequences of chest radiographs of the same patient. METHODS: The reconstruction method is based on a 3D statistical shape model (SSM) of the rib cage, which is adapted to binary 2D projection images of an individual rib cage. To drive the adaptation we minimize a distance measure that quantifies the dissimilarities between 2D projections of the 3D SSM and the projection images of the individual rib cage. We propose different silhouette-based distance measures and evaluate their suitability for the adaptation of the SSM to the projection images. RESULTS: An evaluation was performed on 29 sets of biplanar binary images (posterior-anterior and lateral). Depending on the chosen distance measure, our experiments on the combined reconstruction of shape and pose of the rib cages yield reconstruction errors from 2.2 to 4.7 mm average mean 3D surface distance. Given a geometry of an individual rib cage, the rotational errors for the pose reconstruction range from 0.1 degrees to 0.9 degrees. CONCLUSIONS: The results show that our method is suitable for the estimation of pose differences of the human rib cage in binary projection images. Thus, it is able to provide crucial 3D information for registration during the generation of 2D subtraction images.

Automatic extraction of mandibular nerve and bone from cone-beam CT data.

The exact localization of the mandibular nerve with respect to the bone is important for applications in dental implantology and maxillofacial surgery. Cone beam computed tomography (CBCT), often also called digital volume tomography (DVT), is increasingly utilized in maxillofacial or dental imaging. Compared to conventional CT, however, soft tissue discrimination is worse due to a reduced dose. Thus, small structures like the alveolar nerves are even harder recognizable within the image data. We show that it is nonetheless possible to accurately reconstruct the 3D bone surface and the course of the nerve in a fully automatic fashion, with a method that is based on a combined statistical shape model of the nerve and the bone and a Dijkstra-based optimization procedure. Our method has been validated on 106 clinical datasets: the average reconstruction error for the bone is 0.5 +/- 0.1 mm, and the nerve can be detected with an average error of 1.0 +/- 0.6 mm.

CT-based patient-specific modeling of glenoid rim defects: a feasibility study.

OBJECTIVE: Reconstruction of glenoid bone defects requires accurate preoperative planning. The purpose of this study is to present a method for quantifying the defect size and generating a 3D model of the bone graft for augmentation by matching the fractured glenoid with the contralateral side. MATERIALS AND METHODS: Ten paired shoulders from five cadavers (subjects: three women and two men; mean age, 85 years) and 60 paired shoulders in 30 patients (controls: nine women and 21 men; mean age, 21 years) were examined using CT to determine bilateral comparability by assessment of the maximum glenoid diameters, surface area, and volume. After creation of a glenoid rim defect in the study group, repeated CT scans were superimposed with the data from the contralateral side. The defect size was quantified and the missing fragment virtually reconstructed. Accuracy was evaluated by comparing the virtually repaired glenoid with the predefect CT scan. RESULTS: There were no significant side-to-side differences in intact shoulders (p < 0.05). After creation of the glenoid defects, there was a mean decrease of 31% in the anteroposterior diameter, 34% in surface area, and 19% in volume. The virtually reconstructed glenoids did not differ significantly from the predefect CT scans. The averaged predefect-to-postdefect difference was 3% for the anteroposterior diameter (R(2) = 0.71), 6% for the surface area (R(2) = 0.82), and 4% for the volume (R(2) = 0.98). CONCLUSION: A precise 3D model of the glenoid bony defect can be generated. The computer simulation provides a virtual model of the bone graft, which may potentially improve arthroscopic bone augmentation.

Segmentation of neck lymph nodes in CT datasets with stable 3D mass-spring models segmentation of neck lymph nodes.

RATIONALE AND OBJECTIVES: The quantitative assessment of neck lymph nodes in the context of malignant tumors requires an efficient segmentation technique for lymph nodes in tomographic three-dimensional (3D) datasets. We present a stable 3D mass-spring model for lymph node segmentation in computed tomography (CT) datasets. MATERIALS AND METHODS: For the first time our model concurrently represents the characteristic gray value range, directed contour information, and shape knowledge, which leads to a robust and efficient segmentation process. RESULTS: Our model design and the segmentation accuracy were both evaluated with 40 lymph nodes from five clinical CT datasets containing malignant tumors of the neck. CONCLUSION: The segmentation accuracy proved to be comparable to that of manual segmentations by experienced users and significantly reduced the time and interaction needed for the lymph node segmentation.

Segmentation of neck lymph nodes in CT datasets with stable 3D mass-spring models.

The quantitative assessment of neck lymph nodes in the context of malign tumors requires an efficient segmentation technique for lymph nodes in tomographic 3D datasets. We present a Stable 3D Mass-Spring Model for lymph node segmentation in CT datasets. Our model for the first time represents concurrently the characteristic gray value range, directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. Our model design and segmentation accuracy are both evaluated with lymph nodes from clinical CT neck datasets.