Preoperative Volume Determination for Pituitary Adenoma
This work addresses the need for faster preoperative volume determination in neurosurgery, though it is incremental as it adapts an existing method to a more challenging domain.
The authors tackled the problem of time-consuming manual segmentation of pituitary adenomas in MRI scans by proposing an automated method, achieving an average Dice Similarity Coefficient of 75.92% with a runtime of about one second compared to four minutes for manual segmentation.
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.