joewong00/3D-CNN-Segmentation
Applying 3D CNN for bio-medical images segmentation with 3D-Unet, Residual 3D-Unet and Recurrent Residual 3D-Unet (R2U3D) implemented in PyTorch.
This project helps medical professionals, researchers, and imaging specialists automatically outline specific structures within 3D medical scans, like identifying a kidney in an MRI. You provide 3D medical image files (e.g., MRI scans), and it produces precise 3D masks that highlight the target anatomical features. This is ideal for anyone needing to segment biological structures from volumetric image data for analysis or diagnosis.
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Use this if you need to precisely segment (outline) specific organs or structures in 3D medical images, like MRI or CT scans, to aid in diagnosis, treatment planning, or volumetric analysis.
Not ideal if you are working with 2D images, require real-time segmentation for live procedures, or don't have access to GPU hardware for training and prediction.
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Jul 12, 2022
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