ArticlePloS one2012
Pituitary adenoma volumetry with 3D Slicer.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics of pituitary adenoma using computer vision: a review.Medical & biological engineering & computing · 2024Pooled it
- Trial
- Lateral ventricular choroid plexus enlargement in patients with spontaneous intracranial hypotension.Headache · 2026Article
- Systematic review of pituitary gland and pituitary adenoma automatic segmentation techniques in magnetic resonance imaging.Frontiers in radiology · 2026Review
- Utilities of 3D technology for planning endoscopic endonasal approaches. Case series of anatomical-imaging review.Journal of surgical case reports · 2025Article
- Volumetric measurement of cranial cavity and cerebral ventricular system with 3D Slicer software based on CT data.BMC medical imaging · 2025Article
- Article
- Sex estimation using maxillary sinus volume for Chinese subjects based on cone-beam computed tomography.BMC oral health · 2024Article
- deepPGSegNet: MRI-based pituitary gland segmentation using deep learning.Frontiers in endocrinology · 2024Article
- Automated volumetric assessment of pituitary adenoma.Endocrine · 2024Article
- Manual Segmentation of the Human Choroid Plexus Using Brain MRI.Journal of visualized experiments : JoVE · 2023Article
- Comparing MRI volume measurement techniques for pituitary macroadenoma: Investigating volume reduction and its relationship with biochemical control.Journal of medicine and life · 2023Article
- Fully automated imaging protocol independent system for pituitary adenoma segmentation: a convolutional neural network-based model on sparsely annotated MRI.Neurosurgical review · 2023Article
- Laser interstitial thermal therapy in the treatment of brain metastases: the relationship between changes in postoperative magnetic resonance imaging characteristics and tumor recurrence.Acta neurochirurgica · 2023Article
- Pituitary adenoma consistency affects postoperative hormone function: a retrospective study.BMC endocrine disorders · 2023Article
- Sex Determination Using Human Sphenoid Sinus in a Northeast Iranian Population: A Discriminant Function Analysis.Journal of dentistry (Shiraz, Iran) · 2023Article
- Early Response to Chemotherapy in Malignant Pleural Mesothelioma Evaluated Using Diffusion-Weighted Magnetic Resonance Imaging: Initial Observations.JTO clinical and research reports · 2021Article
- Three-Dimensional Semantic Segmentation of Pituitary Adenomas Based on the Deep Learning Framework-nnU-Net: A Clinical Perspective.Micromachines · 2021Article
- Usefulness of the Texture Signatures Based on Multiparametric MRI in Predicting Growth Hormone Pituitary Adenoma Subtypes.Frontiers in oncology · 2021Article
- Increase in Brain Volume After Aneurysmal Subarachnoid Hemorrhage Leads to Unfavorable Outcome: A Retrospective Study Quantified by CT Scan.Frontiers in neurology · 2021Article
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4 authors.
Funding
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±3.39%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.