Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

Humera Azam; Humera Tariq; Danish Shehzad; Saad Akbar; Habib Shah; Zamin Ali Khan

doi:10.3390/brainsci13091255

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

Brain Sci. 2023 Aug 28;13(9):1255. doi: 10.3390/brainsci13091255.

Authors

Humera Azam¹, Humera Tariq², Danish Shehzad³, Saad Akbar⁴, Habib Shah⁵, Zamin Ali Khan⁶

Affiliations

¹ Department of Computer Science, University of Karachi, Karachi 75270, Pakistan.
² Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
³ Department of Computer Science, The Superior University, Lahore 54590, Pakistan.
⁴ College of Computing and Information Sciences, Karachi Institute of Economics and Technology, Karachi 75190, Pakistan.
⁵ Department of Computer Science, College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia.
⁶ Department of Computer Science, IQRA University, Karachi 71500, Pakistan.

Abstract

This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask-RCNN) for all anatomical orientations of brain MR images. We trained the system from scratch to build a model for classification, detection, and segmentation. It is validated by images taken from three different datasets: BrainWeb; NAMIC, and a local hospital. We opted for purposive sampling to select 2000 images of T1 modality from data volumes followed by a multi-stage random sampling technique to segregate the dataset into three batches for training (75%), validation (15%), and testing (10%) respectively. We utilized a robust backbone architecture, namely ResNet-101 and Functional Pyramid Network (FPN), to achieve optimal performance with higher accuracy. We subjected the same data to two traditional methods, namely Brain Extraction Tools (BET) and Brain Surface Extraction (BSE), to compare their performance results. Our proposed method had higher mean average precision (mAP) = 93% and content validity index (CVI) = 0.95%, which were better than comparable methods. We contributed by training Mask-RCNN from scratch for generating reusable learning weights known as transfer learning. We contributed to methodological novelty by applying a pragmatic research lens, and used a mixed method triangulation technique to validate results on all anatomical modalities of brain MR images. Our proposed method improved the accuracy and precision of skull stripping by fully automating it and reducing its processing time and operational cost and reliance on technicians. This research study has also provided grounds for extending the work to the scale of explainable artificial intelligence (XAI).

Keywords: MRI; brain magnetic resonance images; deep learning; fully automated skull stripping; region-based segmentation.

Grants and funding

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the Small Group Research Project under grant number RGP.1/400/44.