Magnetic Resonance Imaging Brain Segmentation Using Bi-Directional Convolutional Long Short-Term Memory U-Net With Densely Connected Convolutions - Meshari D. Alanazi; Amna Maraoui; Imen Werda; Ahmed Ben Atitallah; Turki M. Alanazi; Mohammed Albekairi; Anis Sahbani; Amr Yousef (IEEE Access)
Despite advancements in imaging technologies, traditional diagnostic methods relying on manual interpretation by clinicians remain prone to errors. This underscores the need for robust automated segmentation techniques. Accurate three-dimensional (3D) Magnetic Resonance Imaging (MRI) brain tumor segmentation is critical for effective diagnosis in neuroimaging. Given the complexity of brain tumors, deep learning approaches have emerged as more suitable than traditional methods for efficient and precise segmentation. The dual difficulties of high computing needs in 3D convolutional networks and the necessity of more precise brain tumor diagnosis are addressed by this work. We provide an artificial intelligence (AI) framework combining bi-directional convolutional long short-term memory (ConvLSTM) layers with densely connected convolutions (DCC). Our method enhances spatial feature learning employing dense connections, and catches complex temporal links across MRI slices. Particularly for the identification of the Enhancing Tumor (ET) region, this novel combination solves the complexity limitation of convolutional techniques, hence improving segmentation robustness. Our approach is validated by extensive testing on the Brain Tumor Segmentation (BraTS) 2020 and 2021 datasets. On the BraTS 2021 dataset, the proposed M-BDCU-Net model beats many state-of-the-art approaches for particular tumor sub-regions with Dice Similarity Coefficient (DSC) scores of 0.81 for Tumor Core (TC), 0.85 for Enhancing Tumor (ET), and 0.82 for Whole Tumor (WT). With 8.81 million parameters and 60.34 GFLOPs, our model also greatly lowers complexity by up to 15% and balances accuracy with efficiency. It is thus much lighter than current models. These results highlight how rapid, more dependable, and exact brain tumor detection our system could allow. This work fills important voids in present research by combining methodological innovation with exhaustive evaluation and provides a useful answer for therapeutic uses.