#5550. Multi-level Kronecker Convolutional Neural Network (ML-KCNN) for Glioma Segmentation from Multi-modal MRI Volumetric Data

August 2026publication date
Proposal available till 21-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Radiology, Nuclear Medicine and Imaging;
Radiological and Ultrasound Technology;
Computer Science Applications;
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Abstract:
The development of an automated glioma segmentation system from MRI volumes is a difficult task because of data imbalance problem. The ability of deep learning models to incorporate different layers for data representation assists medical experts like radiologists to recognize the condition of the patient and further make medical practices easier and automatic. State-of-the-art deep learning algorithms enable advancement in the medical image segmentation area, such a segmenting the volumes into sub-tumor classes. For this task, fully convolutional network (FCN)-based architectures are used to build end-to-end segmentation solutions. In this paper, we proposed a multi-level Kronecker convolutional neural network (MLKCNN) that captures information at different levels to have both local and global level contextual information. Our ML-KCNN uses Kronecker convolution, which overcomes the missing pixels problem by dilated convolution. Moreover, we used a post-processing technique to minimize false positive from segmented outputs, and the generalized dice loss (GDL) function handles the data-imbalance problem.
Keywords:
Brain tumor segmentation; CNN; CRF; Deep learning; FCN; Kronecker convolution

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