Unsupervised registration of 3D knee implant components to biplanar X-ray images.

Background: Registration of three-dimensional (3D) knee implant components to radiographic images provides the 3D position of the implants which aids to analyze the component alignment after total knee arthroplasty.

Methods: We present an automatic 3D to two-dimensional (2D) registration using biplanar radiographic images based on a hybrid similarity measure integrating region and edge-based information. More precisely, this measure is herein defined as a weighted combination of an edge potential field-based similarity, which represents the relation between the external contours of the component projections and an edge potential field estimated on the two radiographic images, and an object specificity property, which is based on the distinction of the region-label inside and outside of the object.

CoSOV1Net: A Cone- and Spatial-Opponent Primary Visual Cortex-Inspired Neural Network for Lightweight Salient Object Detection.

Salient object-detection models attempt to mimic the human visual system's ability to select relevant objects in images. To this end, the development of deep neural networks on high-end computers has recently achieved high performance. However, developing deep neural network models with the same performance for resource-limited vision sensors or mobile devices remains a challenge.

3D biplanar reconstruction of lower limbs using nonlinear statistical models.

Three-dimensional (3D) reconstruction of lower limbs is of great interest in surgical planning, computer assisted surgery, and for biomechanical applications. The use of 3D imaging modalities such as computed tomography (CT) scan and magnetic resonance imaging (MRI) has limitations such as high radiation and expense. Therefore, three-dimensional reconstruction methods from biplanar X-ray images represent an attractive alternative.

Salient Object Detection by LTP Texture Characterization on Opposing Color Pairs under SLICO Superpixel Constraint.

The effortless detection of salient objects by humans has been the subject of research in several fields, including computer vision, as it has many applications. However, salient object detection remains a challenge for many computer models dealing with color and textured images. Most of them process color and texture and therefore implicitly consider them as independent features which is not the case in reality.

Unraveling reproducible dynamic states of individual brain functional parcellation.

Data-driven parcellations are widely used for exploring the functional organization of the brain, and also for reducing the high dimensionality of fMRI data. Despite the flurry of methods proposed in the literature, functional brain parcellations are not highly reproducible at the level of individual subjects, even with very long acquisitions. Some brain areas are also more difficult to parcellate than others, with association heteromodal cortices being the most challenging.