spsingh37/3D_Liver_Tumor_segmentation
This project compares the performance of UNet, ResUNet, SegResNet, and UNETR architectures on the 2017 LiTS dataset for liver tumor segmentation. We evaluate segmentation accuracy using the DICE score to identify key factors for effective tumor segmentation.
This project helps medical imaging specialists and researchers accurately identify liver tumors in 3D medical scans. It takes raw 3D liver CT or MRI scans as input and outputs precise segmentations (outlines) of tumors within those scans. This is useful for radiologists, oncologists, and medical researchers needing highly accurate tumor localization for diagnosis, treatment planning, or research.
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Use this if you need to evaluate or apply advanced deep learning models for segmenting liver tumors from 3D medical imaging data.
Not ideal if you are looking for a ready-to-use clinical tool or if you do not have technical expertise in deep learning and Python to set up and run the models.
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Aug 14, 2024
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