Yibo Dan1, Hongyue Tao2, Chengxiu Zhang1, Chenglong Wang1, Yida Wang1, Shuang Chen2, and Guang Yang1
1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, shanghai, China, 2Department of Radiology, Huashan Hospital, Fudan University, shanghai, China
1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, shanghai, China, 2Department of Radiology, Huashan Hospital, Fudan University, shanghai, China
A pipeline was built to automatically
segment cartilage and subchondral bone regions from FS-PD MRI images and use
the features extracted from those regions to identify chronical ankle joint
instability.
Figure 2: The results of automatic
segmentation. The red contours are the cartilage regions and the green lines
are the subchondral bone regions.
(a)the lateral calcaneal surface of the subtalar joint,
(b)the lateral talar surface of the subtalar joint,
(c)the lateral talar surface of the tibiotalar joint,
(d)the lateral tibial surface of the tibiotalar joint.
(e)the medial calcaneal surface of the subtalar
joint,
(f)the medial talar surface of the subtalar joint,
(g)the medial talar
surface of the tibiotalar joint,
(h)the medial tibial surface of tibiotalar joint.
Table 1：Selected features and their
corresponding coefficients in the final model. C1-C8 represent eight cartilage
ROIs, S1-S8 represent eight subchondral bone 5mm ROIs, W denotes wavelet
transform, L denotes Laplacian of Gaussian filtered.