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DeepCONN: patch-wise deep convolutional neural networks for the segmentation of multiple sclerosis brain lesions

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Abstract

Segmentation is a critical process for examining Multiple Sclerosis (MS) brain lesions for diagnosis, follow-up, and prognosis of the disease. The complexity of the manual segmentation increases due to the variability in size, shape, location, intensity, and texture of lesions. It is also subjected to intra and inter-observer variability. It is, therefore desirable to develop an automatic segmentation pipeline that gives an accurate and reliable performance to diagnose the disease at an early stage. This paper presents a Patch-wise Deep Convolutional Neural Network (DeepCONN) to extract the brain lesions of Multiple Sclerosis (MS) from Magnetic Resonance Images (MRI). To enhance the reliability, the DeepCONN framework uses separate convolutional pathways for T1 and T2 sequences with different convolutional filters, and concatenated output is further passed through another set of convolutional filters to get the final resultant output. The performance of the DeepCONN framework is evaluated on medical image computing and computer-assisted intervention (MICCAI 2008 and 2016) challenges which yield less False Positive Rate (FPR) of 0.5% and a high True Positive Rate (TPR) of 74%. Also, the results show that DeepCONN outperforms other methods in terms of accuracy, i.e., 97%, which indicates a more accurate segmentation of MS brain lesions.

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Data availability

The dataset used in the manuscript is taken from two sources: https://shanoir.irisa.fr/shanoir-ng/challenge-request and https://www.nitrc.org/projects/msseg/

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  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147001, India

    Amrita Kaur & Ashima Singh

  2. Computer Science and Engineering Department, University College of Engineering, Punjabi University, Patiala, Punjab, 147001, India

    Lakhwinder Kaur

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Kaur, A., Kaur, L. & Singh, A. DeepCONN: patch-wise deep convolutional neural networks for the segmentation of multiple sclerosis brain lesions. Multimed Tools Appl 83, 24401–24433 (2024). https://doi.org/10.1007/s11042-023-16292-y

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  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16292-y

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