GOAL: Rheumatoid arthritis (RA) is characterized by inflammation within the joint space as well as erosion or destruction of the bone surface. We believe that volumetric (3-D) ultrasound imaging of the joints in conjunction with automated image-analysis tools for segmenting and quantifying the regions of interest can lead to improved RA assessment. METHODS: In this paper, we describe our proposed algorithms for segmenting 1) the 3 -D bone surface and 2) the 3-D joint capsule region. We improve and extend previous 2-D bone extraction methods to 3-D and make our algorithm more robust to the intensity loss due to surface normals facing away from incident acoustic beams. The extracted bone surfaces coupled with a joint-specific anatomical model are used to initialize a coarse localization of the joint capsule region. The joint capsule segmentation is refined iteratively utilizing a probabilistic speckle model. RESULTS: We apply our methods on 51 volumes from 8 subjects, and validate segmentation results with expert annotations. We also provide the quantitative comparison of our bone detection with magnetic resonance imaging. These automated methods have achieved average sensitivity/precision rates of 94%/93% for bone surface detection, and 87%/83% for joint capsule segmentation. Segmentations of normal and inflamed joints are compared to demonstrate the potential of using proposed tools to assess RA pathology at the joint level. CONCLUSION: The proposed image-analysis methods showed encouraging results as compared to expert annotations. SIGNIFICANCE: These computer-assisted tools can be used to help visualize 3-D anatomy in joints and help develop quantitative measurements toward RA assessment.
GOAL: Rheumatoid arthritis (RA) is characterized by inflammation within the joint space as well as erosion or destruction of the bone surface. We believe that volumetric (3-D) ultrasound imaging of the joints in conjunction with automated image-analysis tools for segmenting and quantifying the regions of interest can lead to improved RA assessment. METHODS: In this paper, we describe our proposed algorithms for segmenting 1) the 3 -D bone surface and 2) the 3-D joint capsule region. We improve and extend previous 2-D bone extraction methods to 3-D and make our algorithm more robust to the intensity loss due to surface normals facing away from incident acoustic beams. The extracted bone surfaces coupled with a joint-specific anatomical model are used to initialize a coarse localization of the joint capsule region. The joint capsule segmentation is refined iteratively utilizing a probabilistic speckle model. RESULTS: We apply our methods on 51 volumes from 8 subjects, and validate segmentation results with expert annotations. We also provide the quantitative comparison of our bone detection with magnetic resonance imaging. These automated methods have achieved average sensitivity/precision rates of 94%/93% for bone surface detection, and 87%/83% for joint capsule segmentation. Segmentations of normal and inflamed joints are compared to demonstrate the potential of using proposed tools to assess RA pathology at the joint level. CONCLUSION: The proposed image-analysis methods showed encouraging results as compared to expert annotations. SIGNIFICANCE: These computer-assisted tools can be used to help visualize 3-D anatomy in joints and help develop quantitative measurements toward RA assessment.