3D Liver Segmentation Dataset

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3D Liver Segmentation Dataset

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3D Liver Segmentation Dataset

Use Case

3D Liver Segmentation

Description

Explore our 3D Liver Tumor Segmentation Dataset, featuring high-resolution NIfTI scans and precise tumor labels.

Description:

The 3D Liver Segmentation dataset is designed for the challenging task of segmenting liver tumors in 3D medical scans. This dataset is particularly useful for developing and evaluating algorithms in the field of medical image analysis, specifically in the automatic detection and segmentation of liver tumors.

Dataset Structure

Input Data (imagesTr): The dataset contains NIfTI files, each representing a 3D medical scan. Each NIfTI file consists of multiple 2D slices, providing comprehensive volumetric data for a single patient scan. These slices capture various cross-sections of the liver, offering detailed anatomical information essential for precise segmentation tasks.
Output Data (labelsTr): Accompanying each input scan, the dataset includes corresponding ground truth labels. These labels are also in NIfTI format and identify the exact location of liver tumors within the 3D scans. The labels serve as a reference for training and validating segmentation algorithms, marking the regions of interest (ROIs) where tumors are localized.

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Key Features

High-Resolution 3D Scans: The dataset comprises high-resolution volumetric scans, allowing for detailed examination and segmentation of liver structures and tumors.
Tumor Localization: Each label file precisely delineates the tumor regions, making this dataset ideal for both supervised learning tasks and benchmarking segmentation algorithms.
Medical Imaging Format: The use of NIfTI format ensures compatibility with most medical imaging software and tools, facilitating easy integration into various machine learning workflows.

Applications

This dataset is an invaluable resource for researchers and developers working on:

Medical Image Segmentation: Developing algorithms that can accurately segment liver tumors from surrounding tissues.
Computer-Aided Diagnosis (CAD): Enhancing diagnostic tools to assist radiologists in identifying and quantifying liver tumors.
Radiomics: Extracting quantitative features from medical images to support personalized medicine and predictive modeling in oncology.
Deep Learning Research: Providing a rich dataset for training and validating deep learning models focused on 3D image analysis.