Department of Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, 10029, New York, NY, USA.
Department of Medicine, University of Michigan Medical School, 1500 E Medical Center Dr, 48109, Ann Arbor, MI, USA.
Pituitary. 2022 Dec;25(6):842-853. doi: 10.1007/s11102-022-01255-7. Epub 2022 Aug 9.
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.
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.
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.
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.
在普通人群中，垂体腺瘤的估计发病率为 10-30%，但放射诊断仍然具有挑战性。诊断的复杂性在于正常（例如复杂的解剖结构、妊娠）和病理状态（例如原发性内分泌病、垂体炎）下的放射特征的异质性。临床症状和实验室检查通常不明确，这可能导致误诊或不必要的专科转诊。计算机视觉模型可以辅助垂体腺瘤的诊断；然而，模型开发的一个主要挑战是缺乏专门的垂体成像数据集。我们假设，从现有的全脑 MRI 扫描中提取鞍区和鞍旁区域的深度容积分割模型可以用于生成新的垂体成像数据集。
纳入了 6 个用于研究目的的开源全脑 MRI 数据集用于模型开发。使用来自单个开源 MRI 数据集的 318 张手动标注 MRI 扫描对基于深度学习的容积分割模型进行训练。然后在来自 5 个保留的研究数据集的 418 张 MRI 上测试模型的分布外容积分割性能。
在我们的标注图像上，手动和模型容积分割之间的一致性很高。在分布内和分布外模型测试中，Dice 评分（一种衡量重叠的指标）范围分别为 0.76-0.82。总共从 6 个数据源纳入了 6755 张 MRI 用于最终生成的垂体数据集。
我们首次构建了最大的使用现有 MRI 数据和深度容积分割模型构建的垂体成像数据集，这些模型旨在识别鞍区和鞍旁解剖结构。该模型很好地泛化到了不同的患者群体和 MRI 扫描仪类型。我们希望我们的垂体数据集将成为未来垂体病变的机器学习研究不可或缺的一部分。
