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Generating novel pituitary datasets from open-source imaging data and deep volumetric segmentation

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Abstract

Purpose

The estimated incidence of pituitary adenomas in the general population is 10–30%, yet radiographic diagnosis remains a challenge. Diagnosis is complicated by the heterogeneity of radiographic features in both normal (e.g. complex anatomy, pregnancy) and pathologic states (e.g. primary endocrinopathy, hypophysitis). Clinical symptoms and laboratory testing are often equivocal, which can result in misdiagnosis or unnecessary specialist referrals. Computer vision models can aid in pituitary adenoma diagnosis; however, a major challenge to model development is the lack of dedicated pituitary imaging datasets. We hypothesized that deep volumetric segmentation models trained to extract the sellar and parasellar region from existing whole-brain MRI scans could be used to generate a novel dataset of pituitary imaging.

Methods

Six open-source whole-brain MRI datasets, created for research purposes, were included for model development. Deep learning-based volumetric segmentation models were trained using 318 manually annotated MRI scans from a single open-source MRI dataset. Out-of-distribution volumetric segmentation performance was then tested on 418 MRIs from five held-out research datasets.

Results

On our annotated images, agreement between manual and model volumetric segmentations was high. Dice scores (a measure of overlap) ranged 0.76–0.82 for both in-distribution and out-of-distribution model testing. In total, 6,755 MRIs from six data sources were included in the final generated pituitary dataset.

Conclusions

We present the first and largest dataset of pituitary imaging constructed using existing MRI data and deep volumetric segmentation models trained to identify sellar and parasellar anatomy. The model generalizes well across patient populations and MRI scanner types. We hope our pituitary dataset will be an integral part of future machine learning research on pituitary pathologies.

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Funding

This work was supported by the Neurosurgery Research & Education Foundation (2021 Medical Student Summer Research Fellowship to R. Gologorsky).

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Authors and Affiliations

  1. Department of Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, 10029, New York, NY, USA

    Rachel Gologorsky

  2. Department of Medicine, University of Michigan Medical School, 1500 E Medical Center Dr, 48109, Ann Arbor, MI, USA

    Edward Harake

  3. Department of Neurosurgery, NYU Langone Health System, 530 First Ave, 10016, New York, NY, USA

    Grace von Oiste, Mustafa Nasir-Moin & Eric Oermann

  4. Department of Neurosurgery, University of Utah, 201 Presidents’ Cir, 84132, Salt Lake City, UT, USA

    William Couldwell

  5. Department of Radiology, NYU Langone Health System, 530 First Ave, 10016, New York, NY, USA

    Eric Oermann

  6. Center for Data Science, New York University, 60 5th Ave, 10011, New York, NY, USA

    Eric Oermann

  7. Machine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, 1500 E Medical Center Dr, 48109, Ann Arbor, MI, USA

    Todd Hollon

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  1. Rachel Gologorsky
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Correspondence to Todd Hollon.

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The authors report no relevant conflicts of interest.

Compliance with ethical standards

While our research was done using human MRI data, these data were anonymized and are publicly available. No informed consent was required to complete the study.

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Gologorsky, R., Harake, E., von Oiste, G. et al. Generating novel pituitary datasets from open-source imaging data and deep volumetric segmentation. Pituitary 25, 842–853 (2022). https://doi.org/10.1007/s11102-022-01255-7

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