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liver segmentation using pytorch and monai to detect tumor

Liver Segmentation using PyTorch and MONAI

Deep learning can be used to detect diseases like cancer by finding tumor cells through medical images. Deep learning also has the potential to improve the quality of medical care by segmenting organs during surgery or scanning patients for signs of cancer or other ailments. So the goal of this project is to use Monai and PyTorch with the Python programming language to create a deep learning model to segment a liver from a public CT scan dataset. We can use the same model that segments the liver, as well as use the same principle to segment other organs or tumors from CT scans or MRIs.

Liver-Segmentation-for-medical-Image-Processing-Using-Monai-and-Pytorch

Liver segmentation using monai and pytorch

Using PyTorch and MONAI created an Image Segmentation Model that segments the human liver from a Public CT SCAN Dataset.

This project aims to develop an efficient and accurate liver segmentation solution using the PyTorch deep learning framework and the MONAI library.

Machine Learning use case in the field of Healthcare. In this project MONAI and pytorch frameworks are used for 3D Liver segmentation.

Liver Segmentation Using Monai and PyTorch

Liver-Segmentation-Using-Monai-and-PyTorch

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Algorithm for automatic liver segmentation.

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This project is made using Python for computer vision, Pytorch and monai for healthcare imaging targeting automatic liver segmentation.

Developed a 3D U-Net-based liver segmentation model using MONAI and PyTorch Preprocessed CT scans (resampling, normalization, DICOM → NIfTI) Trained and evaluated models using Dice loss and Dice score metrics Enabled GPU-accelerated training and automated evaluation pipelines