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The proposed approach aims to segment tumours inside the liver envelope automatically with a level of accuracy adequate for its use as a tool for surgical planning using abdominal CT images. The approach will be validated on larger datasets in the future.

Diabetic retinopathy (DR) is a common ophthalmic disease among diabetic patients. It is essential to diagnose DR in the early stages of treatment. Various imaging systems have been proposed to detect and visualize retina diseases. The fluorescein angiography (FA) imaging technique is now widely used as a gold standard technique to evaluate the clinical manifestations of DR. Optical coherence tomography (OCT) imaging is another technique that provides 3D information of the retinal structure. The FA and OCT images are captured in two different phases and field of views and image fusion of these modalities are of interest to clinicians. This paper proposes a hybrid registration framework based on the extraction and refinement of segmented major blood vessels of retinal images. The newly extracted features significantly improve the success rate of global registration results in the complex blood vessel network of retinal images. Afterward, intensity-based and deformable transformations are utilized to further compensate the motion magnitude between the FA and OCT images. Experimental results of 26 images of the various stages of DR patients indicate that this algorithm yields promising registration and fusion results for clinical routine.

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The introduced planning method helped us prove that it is possible to use such a "general purpose" redundant manipulator equipped with a dedicated tool to perform percutaneous interventions in cluttered spaces like a CT gantry.