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Thuy Dao edited this page Jan 15, 2024 · 1 revision

Automatic Medical Image Segmentation and Annotation Web App

Overview

The Automatic Medical Image Segmentation and Annotation Web App is a powerful client-side application designed to streamline the process of segmenting and annotating medical images. This innovative tool supports various formats, including NIfTI (Neuroimaging Informatics Technology Initiative) and DICOM (Digital Imaging and Communications in Medicine), providing healthcare professionals with a user-friendly and efficient solution for image analysis.

Key Features

  1. Format Compatibility The web app supports popular medical image formats, including:

Voxel-based formats: NIfTI, NRRD, MRtrix MIF, AFNI HEAD/BRIK, MGH/MGZ, ITK MHD, ECAT7.

Mesh-based formats: GIfTI, ASC, BYU/GEO/G, BrainSuite DFS, ICO/TRI, PLY, BrainNet NV, BrainVoyager SRF, FreeSurfer, MZ3, OFF, Wavefront OBJ, STL, Legacy VTK, X3D.

Mesh overlay formats: GIfTI, CIfTI-2, MZ3, SMP, STC, FreeSurfer (CURV/ANNOT)

Tractography formats: TCK, TRK, TRX, VTK, AFNI .niml.tract

DICOM: DICOM and DICOM Manifests

  1. Automatic Segmentation The application leverages state-of-the-art algorithms to automatically segment medical images, reducing the manual effort required for this crucial task. The segmentation process enhances the accuracy and speed of medical image analysis.

  2. Annotation Capabilities Healthcare professionals can annotate segmented regions with relevant information, such as labels, measurements, and clinical notes. The annotation feature facilitates communication and collaboration among medical practitioners.

  3. Client-Side Processing One of the standout features of this web app is its ability to perform all image processing tasks on the client-side. This ensures data privacy and security, as sensitive medical information does not need to be transmitted over the internet. The client-side processing also enhances performance by utilizing the computing power of the user's device.

  4. User-Friendly Interface The web app boasts an intuitive and user-friendly interface, making it accessible to healthcare professionals with varying levels of technical expertise. The design prioritizes ease of use without compromising on the advanced functionalities offered.

  5. Multi-Platform Compatibility The application is designed to run seamlessly on various platforms and devices, including desktops, laptops, and tablets. Its responsive design ensures a consistent user experience across different screen sizes.

How It Works

Upload Medical Image: Users can upload medical images in NIfTI or DICOM format directly through the web interface.

Automatic Segmentation: The application utilizes advanced segmentation algorithms to automatically identify and delineate relevant structures or regions within the uploaded medical images.

Annotation: Healthcare professionals can annotate segmented regions with clinical information using a range of annotation tools provided by the web app.

Download and Share: Once the segmentation and annotation are complete, users can download the annotated images for further analysis or share them with colleagues for collaborative decision-making.

Benefits

Efficiency: Automating the segmentation process reduces the time and effort required for medical image analysis.

Privacy: Client-side processing ensures that sensitive medical data remains on the user's device, enhancing privacy and security.

Collaboration: The annotation feature facilitates collaboration among healthcare professionals by providing a platform for sharing and discussing annotated images.

Accessibility: The user-friendly interface and multi-platform compatibility make the web app accessible to a wide range of healthcare professionals.

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