Automatic detection of anatomical landmarks on the knee joint using MRI data - Xue, Ning; Döllinger, Michael; Ho, Charles P.; Surowiec, Rachel K.; Schwarz, Raphael (Journal of Magnetic Resonance Imaging, 2015)
To propose a new automated learning-based scheme for locating anatomical landmarks on the knee joint using three-dimensional (3D) MR image data.This method makes use of interest points as candidates for landmarks. All candidates are evaluated by a "coarse to fine" 3D feature descriptor computed from manually placed landmarks in training datasets. The results are refined using a multi-classifier boosting system. We demonstrate our method by the detection of 24 landmarks on the knee joint of 35 subjects. To verify the robustness, the test datasets differ in contrast, resolution, patient positioning, and health condition of the knee joint. The proposed method is evaluated by measuring the distance between manually placed landmarks and automatically detected landmarks and the computational cost for detecting one landmark in a 3D dataset.The results reveal that the method is capable of localizing landmarks with a reasonable accuracy (1.64 ± 1.03 mm [mean ± standard deviation]), sensitivity (97%) and run time efficiency (4.82 s).This study suggests that the proposed method is an accurate and robust approach for the automated landmark detection in various MR datasets. The proposed method can be used as the initialization or constraint in higher level medical image processing workflows such as in kinematic description, segmentation and registration.
EVALuna2:19995

Automatic model-based semantic registration of multimodal MRI knee data - Xue, Ning; Döllinger, Michael; Fripp, Jurgen; Ho, Charles P.; Surowiec, Rachel K.; Schwarz, Raphael (Journal of Magnetic Resonance Imaging, 2015)
To propose a robust and automated model-based semantic registration for the multimodal alignment of the knee bone and cartilage from three-dimensional (3D) MR image data.The movement of the knee joint can be semantically interpreted as a combination of movements of each bone. A semantic registration of the knee joint was implemented by separately reconstructing the rigid movements of the three bones. The proposed method was validated by registering 3D morphological MR datasets of 25 subjects into the corresponding T2 map datasets, and was compared with rigid and elastic methods using two criteria: the spatial overlap of the manually segmented cartilage and the distance between the same landmarks in the reference and target datasets.The mean Dice Similarity Coefficient (DSC) of the overlapped cartilage segmentation was increased to 0.68 ± 0.1 (mean ± SD) and the landmark distance was reduced to 1.3 ± 0.3 mm after the proposed registration method. Both metrics were statistically superior to using rigid (DSC: 0.59 ± 0.12; landmark distance: 2.1 ± 0.4 mm) and elastic (DSC: 0.64 ± 0.11; landmark distance: 1.5 ± 0.5 mm) registrations.The proposed method is an efficient and robust approach for the automated registration between morphological knee datasets and T2 MRI relaxation maps.
EVALuna2:20006