Pituitary Adenoma Volumetry with 3D Slicer

TL;DR

This paper demonstrates that semi-automatic pituitary adenoma volumetry using 3D Slicer significantly reduces segmentation time while maintaining high accuracy compared to manual methods.

Contribution

It introduces a semi-automatic segmentation approach with GrowCut in 3D Slicer for pituitary adenomas, showing improved efficiency and comparable accuracy.

Findings

GrowCut segmentation reduces time by about 30%

Average Dice Similarity Coefficient of 81.97%

Semi-automatic method is less user-intensive

Abstract

In this study, we present pituitary adenoma volumetry using the free and open source medical image computing platform for biomedical research: (3D) Slicer. Volumetric changes in cerebral pathologies like pituitary adenomas are a critical factor in treatment decisions by physicians and in general the volume is acquired manually. Therefore, manual slice-by-slice segmentations in magnetic resonance imaging (MRI) data, which have been obtained at regular intervals, are performed. In contrast to this manual time consuming slice-by-slice segmentation process Slicer is an alternative which can be significantly faster and less user intensive. In this contribution, we compare pure manual segmentations of ten pituitary adenomas with semi-automatic segmentations under Slicer. Thus, physicians drew the boundaries completely manually on a slice-by-slice basis and performed a Slicer-enhanced segmentation using the competitive region-growing based module of Slicer named GrowCut. Results showed that the time and user effort required for GrowCut-based segmentations were on average about thirty percent less than the pure manual segmentations. Furthermore, we calculated the Dice Similarity Coefficient (DSC) between the manual and the Slicer-based segmentations to proof that the two are comparable yielding an average DSC of 81.97\pm3.39%.