Automatic Segmentation & Measurement for Hydrocephalus

Artificial Intelligence-Based Diagnosis & Monitoring

QuantBrain - Ventricle can segment, analyze, and monitor ventricles with unparalleled precision and seamlessly integrate into your workflow using the power of AI.

Straightforward Editing & User-Friendly Annotation Tools

  • Refine automatic segmentations directly from your browser using QuantBrain - Ventricle's built-in viewer.
  • Adjust and fine-tune annotations using a variety of editing tools and viewing windows for optimal analysis.
  • QuantBrain - Ventricle's Smart Interpolation tracks annotations on slices and interpolates any changes to unedited slices.

Multi-Modality Support

  • Segment CT, pre- and post-contrast T1, T2, and FLAIR MRI imaging series within QuantBrain - Ventricle for diagnosing and monitoring hydrocephalus.

Fast & Precise Segmentation

  • Using QuantBrain - Ventricle's mean Dice score values of 0.92 & 0.9 for CT and MRI scans, respectively, create precise segmentations in minutes without any hassle.

Impactful Quantitative Value Calculations

  • QuantBrain - Ventricle reports accurate hydrocephalus and shunt malfunction evaluation through various determining metrics, such as ventricular volume and the Evans Index ratio.
  • Results are easily visualized in the form of a generated report.

Longitudinal Tracking

  • QuantBrain - Ventricle enables longitudinal analysis across series in order to track changes over time.

Built-in Quality Assurance

  • QuantBrain - Ventricle's quality estimation feature ensures both accurate segmentations and continuous algorithm refinement for segmentations generated in the future.

Multi-Faceted Clinical Workflow Incorporation

Publications

Establishment of Age- and Sex-Specific Reference Cerebral Ventricle Volumes

Ryan T Kellogg, Min S. Park, M. Harrison Snyder, Alexandria Marino, Sohil Patel, Xue Feng, Jan Vargas

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MRI-Based Prediction of Clinical Improvement after Ventricular Shunt Placement for Normal Pressure Hydrocephalus: Development and Evaluation of an Integrated Multisequence Machine Learning Algorithm

Owen P. Leary, Zhusi Zhong, Lulu Bi, Zhicheng Jiao, Yu-Wei Dai, Kevin Ma, Shanzeh Sayied, Daniel Kargilis, Maliha Imami, Lin-Mei Zhao, Xue Feng, Gerald Riccardello, Scott Collins, Konstantina Svokos, Abhay Moghekar, Li Yang, Harrison Bai, Petra M. Klinge, Jerrold L. Boxerman

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