This impactful CSR project aims to develop and distribute freeware software implementing deep neural networks for automated segmentation and quantification of brain lesions, specifically focusing on Glioma and Multiple Sclerosis lesion segmentation in medical images. Led by IIT Madras, the initiative provides radiology centers with a valuable tool for automated and highly reproducible lesion analysis, reducing manual labor, minimizing the risk of errors, and enabling large clinical studies that

Freeware for Brain Lesion Analysis

Objective

• This project aims to develop and distribute a freeware software package implementing deep neural networks for the segmentation and quantification of brain lesions, specifically focusing on Glioma or brain tumor segmentation and Multiple Sclerosis (MS) lesion segmentation in medical images. The objective is to provide radiology centers with a valuable tool for automated and highly reproducible lesion analysis, enhancing patient care, enabling large clinical studies, and facilitating further research in medical image analysis.

Description

• Develop a user-friendly software interface for brain lesion segmentation using deep neural networks., Make the software DICOM compatible for seamless integration into clinical workflows., Test the software on limited data to ensure accuracy and reliability., Install the software in qualifying hospitals, starting with Sree Chitra Thirunal Institute of Medical Science and Technology, Trivandrum., Continuously update and improve the software to handle additional image analysis tasks in the future.

Impact

• The project's impact is significant as it addresses the need for efficient and accurate brain lesion analysis in medical imaging. By providing a freeware solution, radiology centers can benefit from automated lesion segmentation, reducing manual labor and the risk of errors. This tool can aid in clinical decision-making, monitor disease progression, and enhance patient care for individuals with Gliomas and Multiple Sclerosis. Furthermore, the software's scalability and potential for handling other image analysis tasks make it a valuable resource for medical research and diagnostic centers.