Preoperative Volume Determination for Pituitary Adenoma

TL;DR

This paper introduces an automated segmentation method for pituitary adenomas in MRI scans, significantly reducing segmentation time while maintaining comparable accuracy to manual methods.

Contribution

The study adapts a glioblastoma segmentation algorithm for pituitary adenomas, addressing challenges due to lack of contrast-enhanced boundaries.

Findings

Average DSC of 75.92% indicating good segmentation accuracy

Automated method reduces segmentation time from four minutes to one second

Method demonstrates potential for efficient preoperative volume determination

Abstract

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.