Shape analysis is an important and powerful tool in a wide variety of medical applications. Many shape analysis techniques require shape representations which are in correspondence. Unfortunately, popular techniques for generating shape representations do not handle objects with complex geometry or topology well, and those that do are not typically readily available for non-expert users. We describe a method for generating correspondences across a population of objects using a given template. We also describe its implementation and distribution via SlicerSALT, an open-source platform for making powerful shape analysis techniques more widely available and usable. Finally, we show results of this implementation on mouse femur data.
The most common sellar lesion is the pituitary adenoma, and sellar tumors are approximately 10-15% of all intracranial
neoplasms. Manual slice-by-slice segmentation takes quite some time that can be reduced by using the appropriate
algorithms. In this contribution, we present a segmentation method for pituitary adenoma. The method is based on an
algorithm that we have applied recently to segmenting glioblastoma multiforme. A modification of this scheme is used
for adenoma segmentation that is much harder to perform, due to lack of contrast-enhanced boundaries. In our
experimental evaluation, neurosurgeons performed manual slice-by-slice segmentation of ten magnetic resonance
imaging (MRI) cases. The segmentations were compared to the segmentation results of the proposed method using the
Dice Similarity Coefficient (DSC). The average DSC for all datasets was 75.92%±7.24%. A manual segmentation took
about four minutes and our algorithm required about one second.
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