Meeting Banner
Abstract #4527

Post-Surgical Cavity Segmentation on MRI for Glioma Patients with Deep Learning and Transfer Learning Approach

Virendra Kumar Yadav1, Ankit Kandpal1, Raufiya Jafari1, Rakesh Kumar Singh2, Rakesh Kumar Gupta2, Sumeet Agarwal3,4, and Anup Singh1,4,5
1Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, New Delhi, India, 2Department of Radiology, Fortis Memorial Research Institute, Gurugram, India, 3Department of Electrical Engineering, Indian Institute of Technology, Delhi, New Delhi, India, 4Yardi School of Artificial Intelligence, Indian Institute of Technology, New Delhi, India, 5Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India

Synopsis

Keywords: Diagnosis/Prediction, Tumor, Post cavity segmentation

Motivation: Currently, manual/semiautomatic methods are being used for post-surgical cavity segmentation in glioma patients, which is crucial for precise treatment planning and monitoring.

Goal(s): To evaluate the potential of deep learning models for automatic post-surgical cavity segmentation.

Approach: Deep learning models were trained on pre-surgery and post-surgery (within 72hours) MRI data and tested on post-surgery data. The concept of data augmentation and transfer learning were also used.

Results: Initially trained on the pre-surgery BraTS’2021 challenge dataset, the models underwent further refinement with local hospital's post-surgical data, resulting in 36% improvement in post-surgical cavity segmentation (U-Net3, DSC = 0.64).

Impact: This study demonstrates the potential of deep-learning in automatic segmentation of post-surgical cavity on MRI images. Proposed automatic segmentation models can assist in fast and objective treatment planning. Further optimizations for improved accuracy and validation on large dataset is required.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords