A Proposed CNN-based Hybrid Deep Learning Model for the Segmentation of Multi-organ Functional Tissue Units
Keywords:
Multi-Organ Segmentation, Functional Tissue Units, Deep Learning, U-Net Architecture, Convolutional Neural Networks, Image MaskingAbstract
In order to provide effective techniques for identifying and segmenting organs in high-resolution histology pictures, this research paper introduces a deep learning approach to the HubMAP-Organ Segmentation Competition. The proposed solution combines a Convolutional Neural Network with a U-Net architecture to achieve state-of-the-art performance on the competition’s validation dataset. Extensive experiments have also been performed to analyze the effect of different hyperparameters and preprocessing techniques on the model performance and also the usage of different pre-trained models such as AlexNet, ZFNet, and EfficientNet. Our proposed CNN-based hybrid model claims to achieve a mean Dice Coefficient/Accuracy of 0.84178 on each image in the test set highlighting the effectiveness of our approach and providing insights into organ segmentation in high-resolution histology images, which has important applications in medical diagnosis and research.
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