Browsing by Author "S. Niyas"

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    3d Convolutional Neural Network Architectures for Volumetric Medical Image Segmentation
    (National Institute of Technology Karnataka, Surathkal., 2024) S. Niyas
    Computer-aided medical image analysis plays a critical role in supporting medical practitioners with expert clinical diagnoses and determining optimal treatment plans. Currently, convolu tional neural networks (CNNs) are widely regarded as the preferred method for automated medical image analysis due to their ability to autonomously learn relevant features from train ing data. However, most cutting-edge semantic image segmentation techniques rely on two dimensional (2D) CNN models, which do not fully exploit the inter-slice information available in cross-sectional imaging modalities, such as MRI volumes. This limitation underscores the need for more advanced approaches to better utilize the three-dimensional (3D) data inherent in these imaging techniques. In this thesis, we present a comprehensive evaluation of various techniques employed in 3D deep learning for medical image segmentation. With the rapid advancements in 3D imaging systems and excellent hardware and software support to process large volumes of data, 3D deep learning methods are gaining popularity in medical image segmentation. However, traditional 3D CNN-based segmentation models require substantial computational resources, extensive memory, and typically larger datasets than 2D CNN approaches. To address these challenges, we propose a 3D CNN segmentation model that e!ciently extracts information across slices and mitigates several limitations associated with traditional 3D CNN techniques. The method aims to retain the advantages of both 2D CNN and 3D CNN methods by e”ectively designing input data slices and the CNN architecture. In this study, we proposed a shallow sliced stacking approach to reduce the depth of input 3D data to maintain a good segmentation accuracy with minimum computation overhead and model complexity. Incorporating residual connections in the encoder path also facilitates the extraction of multi-scale features without significantly increasing the model complexity. Accurate diagnosis of various medical conditions often requires the simultaneous analysis of multiple image characteristics. For instance, Focal Cortical Dysplasia (FCD) lesion detec viii tion can be significantly enhanced by incorporating data on cortical thickness maps along with f luid-Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI) scans. Ad ditionally, employing multi-axis analysis of 3D cross-sectional imaging can substantially improve diagnostic performance. Inspired by these concepts, we propose a 3D deep learning model em ploying a multi-view, dual encoder-decoder architecture. The model also incorporates various architecture-wise enhancements, including an end-to-end cascaded approach for transitioning from coarse to fine segmentation, 3D Attention modules for maintaining consistency between encoder and decoder pairs, and dual-task learning. In our study, we apply this model to pro cess FLAIR MRI volumes alongside corresponding cortical thickness maps, aiming to e”ectively detect FCD lesions. Generative Adversarial Networks (GANs) have significantly impacted the field of image anal ysis, and they have been successfully employed for tasks such as image segmentation. Hence, this study also proposes a 3D attention-driven Vox2Vox CNN network that leverages the power of a 3D GAN to accurately segment acute stroke lesion cores in Computed Tomography Per fusion (CTP) scans. This methodology also incorporates valuable insights derived from our prior models relevant to this research. The segmentation framework incorporates two super vised GAN components: a generator and a discriminator. The generator module is designed to process 3D slices from CTP maps and learn to generate 3D binary prediction masks that closely match the ground truth for stroke lesions. Concurrently, the discriminator module is trained to distinguish between the outputs generated by the generator and the actual ground truth. Overall, this thesis demonstrates the e!cacy of 3D deep learning in identifying malig nancies from cross-sectional imaging modalities, including CT and MRI, thereby enhancing the capabilities of automated Computer-Aided Detection (CAD) systems.