Pituitary Adenoma Volumetry with 3D Slicer

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%

Automated volumetric assessment of pituitary adenoma

PURPOSE Assessment of pituitary adenoma (PA) volume and extent of resection (EOR) through manual segmentation is time-consuming and likely suffers from poor interrater agreement, especially postoperatively. Automated tumor segmentation and volumetry by use of deep learning techniques may provide more objective and quick volumetry. METHODS We developed an automated volumetry pipeline for pituitary adenoma. Preoperative and three-month postoperative T1-weighted, contrast-enhanced magnetic resonance imaging (MRI) with manual segmentations were used for model training. After adequate preprocessing, an ensemble of convolutional neural networks (CNNs) was trained and validated for preoperative and postoperative automated segmentation of tumor tissue. Generalization was evaluated on a separate holdout set. RESULTS In total, 193 image sets were used for training and 20 were held out for validation. At validation using the holdout set, our models (preoperative / postoperative) demonstrated a median Dice score of 0.71 (0.27) / 0 (0), a mean Jaccard score of 0.53 ± 0.21/0.030 ± 0.085 and a mean 95$^{th}$ percentile Hausdorff distance of 3.89 ± 1.96./12.199 ± 6.684. Pearson's correlation coefficient for volume correlation was 0.85 / 0.22 and -0.14 for extent of resection. Gross total resection was detected with a sensitivity of 66.67% and specificity of 36.36%. CONCLUSIONS Our volumetry pipeline demonstrated its ability to accurately segment pituitary adenomas. This is highly valuable for lesion detection and evaluation of progression of pituitary incidentalomas. Postoperatively, however, objective and precise detection of residual tumor remains less successful. Larger datasets, more diverse data, and more elaborate modeling could potentially improve performance

Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application

In this contribution, we used the GrowCut segmentation algorithm publicly available in three-dimensional Slicer for three-dimensional segmentation of vertebral bodies. To the best of our knowledge, this is the first time that the GrowCut method has been studied for the usage of vertebral body segmentation. In brief, we found that the GrowCut segmentation times were consistently less than the manual segmentation times. Hence, GrowCut provides an alternative to a manual slice-by-slice segmentation process.Comment: 10 page

ADENOMA HIPOFISIS

AbstrakAdenoma hipofisis diklasifikasikan berdasarkan beberapa kriteria yaitu klinis dan endokrin, patologi, serta radiologi. Klasifikasi endokrin membedakan tumor sebagai fungsional dan nonfungsional, berdasarkan aktivitas sekretorinya in-vivo. Klasifikasi patologi berusaha untuk membatasi kelompok tumor heterogenus secara klinis dan patologis dengan kategori yaitu asidofilik, basofilik, dan kromofobik. Klasifikasi radiologi mengelompokkan tumor hipofisis berdasarkan ukuran dan karakteristik pertumbuhan, yang dapat ditemukan dari studi imaging. WHO membuat klasifikasi yang mencoba untuk mengintegrasikan semua klasifikasi yang ada dan menyediakan sinopsis praktis untuk aspek klinis dan patologis dari adenoma. Diagnosa adenoma hipofisis dibuat berdasarkan: gejala klinis dari gangguan hormon, adanya riwayat penyakit dahulu yang jelas, pemeriksaan fisik yang menunjang, pemeriksaan laboratorium yang menunjukkan disfungsi dari hormon yang terganggu, adanya pemeriksaan penunjang yang akurat seperti CTScan, MRI-Scan. Jenis, besar dan fungsi dari tumor sangat menentukan dalam mempertimbangkan penatalaksanaan dari adenoma hipofisis. Pengobatan diindikasikan pada semua pasien dengan gejala, terutama dengan hipogonadisme. Pilihan terapi termasuk kontrol dengan obat-obatan, reseksi bedah, dan terapi radiasi.AbstractPituitary adenomas are classified according to several criteria; clinical endocrine, pathology, and radiology. Endocrine classification distinguishes tumors as functional and nonfunctional, based on in-vivo secretory activity. Pathology classification seeks to restrict clinically heterogeneous group of tumors and pathological categories namely acidophilic, basophilic, and kromofobik. Radiological classification classifies pituitary tumors by size and growth characteristics, which can be found on imaging studies. WHO made a classification that attempts to integrate all existing classifications and provide practical synopsis for the clinical and pathological aspects of adenoma. Diagnosis of pituitary adenoma is made based on: clinical symptoms of hormonal disorders, clear past medical history, clear physical examination, laboratory tests show hormonal dysfunction or disturbance, and accurate investigations such as CT scan and MRI scan. Types, large and function of the tumor is crucial in considering the management of pituitary adenomas. Treatment is indicated in all patients with symptoms, especially with hypogonadism. Therapeutic options include control with drugs, surgical resection and radiation therapy