Commercial

Brain CT Segmentation Dataset

It is a detailed brain CT dataset featuring over 1,000 annotated CT scans for tumor segmentation, brain hemorrhage detection, and other pathology classification tasks, designed to train machine learning models in semantic segmentation, treatment planning, and accurate analysis of brain tissues, structures, and injuries in medical imaging.

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  • medical studies
    1,000+
  • pathologies
    10
Example of a study
  • Medicine
  • Segmentation
  • Computer Vision
  • Classification
  • Machine Learning

It is a detailed brain CT dataset featuring over 1,000 annotated CT scans for tumor segmentation, brain hemorrhage detection, and other pathology classification tasks, designed to train machine learning models in semantic segmentation, treatment planning, and accurate analysis of brain tissues, structures, and injuries in medical imaging.

Get in touch Download sample
  • Medicine
  • Segmentation
  • Computer Vision
  • Classification
  • Machine Learning
  • medical studies
    1,000+
  • pathologies
    10

Dataset Info

Characteristic Data
Description CT scans of the brain to recognize pathologies
Data types Nii
Tasks Recognition, classification and segmentation of pathologies, computer vision
Number of studies 1,000+
Number of pathologies 10
Pathologies Acute ischemia, chronic ischemia, 5 classes of intracranial hemorrhagic stroke (intraventricular, intraparenchymal, epidural, subdural, subarachnoid), subgaleal hematoma, fracture, tumor.
Labeling Segmentation
Example of a study
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Technical
Characteristics

Characteristic Data
File Extension Nii
Source and collection methodology. Data was collected by a partner of Unidata.

Dataset Use Cases

  • Medical Imaging & Radiology

    Tumor Detection and Segmentation in Brain CT Scans

    Brain CT Segmentation Dataset supports research in tumor segmentation using detailed CT images from head CT studies. The dataset contains annotated brain structures and brain tissues, enabling automatic segmentation and semantic segmentation for identifying brain tumors and related abnormalities. It is ideal for improving segmentation accuracy in clinical imaging workflows.

  • Artificial Intelligence & Machine Learning

    Training Deep Learning Models for Brain CT Analysis

    This dataset provides diverse training data for machine learning and learning algorithms aimed at detecting ischemic strokes and brain hemorrhages. The dataset consists of labeled segmentation masks and medical images, helping researchers train models for classification tasks and binary segmentation with the highest accuracy across various CT scanners and imaging conditions.

  • Healthcare & Clinical Applications

    Brain Injury and Stroke Treatment Planning

    In healthcare environments, it helps physicians in treatment planning by providing detailed CT scans and annotated datasets of brain injuries and ischemic strokes. These test datasets enhance diagnostic precision and aid in automatic segmentation of affected brain tissues, improving patient outcomes through faster, data-driven clinical decisions.

  • Biomedical Research & Data Science

    Benchmarking Segmentation Algorithms for Brain CT Imaging

    Researchers use this Brain CT Segmentation Dataset to benchmark segmentation results and evaluate semantic segmentation performance on medical images. The dataset’s structured file format and accurate hemorrhage segmentation annotations enable consistent comparison of deep learning methods, driving innovation in CT imaging and reliable analysis of brain CT datasets for academic and clinical research.

FAQs

What is included in this dataset?
The dataset includes over 1,000 CT scans of the human brain, covering 10 pathology classes such as acute and chronic ischemia, multiple types of intracranial hemorrhages, fractures, and tumors. Each scan is stored in .Nii format and paired with corresponding segmentation masks for detailed analysis.
What types of annotations are provided?
The annotations include precise segmentation masks for pathologies and brain structures, allowing accurate labeling for deep learning models. These detailed annotations are designed to support semantic and binary segmentation tasks, improving the segmentation accuracy of automated models.
Can I request a sample of the dataset before purchasing or downloading it?
Yes. Unidata offers free samples of its datasets for testing and evaluation. The sample allows you to explore a selection of brain CT images and segmentation masks, helping you assess image resolution, annotation accuracy, and relevance before acquiring the full dataset.
Is it possible to request a custom dataset?
Yes. Researchers and companies can request a customized dataset that meets their requirements, such as specific pathologies, image modalities, or annotation formats.
How was the data collected?
The images were collected by a certified Unidata partner using standardized acquisition protocols on high-resolution CT scanners. All data underwent quality control and anonymization, ensuring clinical validity and full compliance with medical imaging ethics and privacy standards.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets are curated in full compliance with GDPR and applicable international privacy laws. The data is obtained exclusively from lawful medical sources, ensuring ethical, anonymized, and privacy-protected handling of sensitive health information.
How are Unidata datasets stored?
All datasets are securely hosted on AWS cloud infrastructure, ensuring scalability, high availability, and robust data protection. Unidata’s management practices, compliant with ISO 27001 and ISO 27701, provide a secure and privacy-focused environment for medical imaging data storage and delivery.
How long does it take to receive the dataset?
After submitting your dataset request, Unidata will contact you to confirm details and complete documentation. Upon signing and payment, the Brain CT Segmentation Dataset is delivered securely within 3–10 business days.
Still have questions about using Unidata datasets? Read our user-guides

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