Enhancing Medical Image Segmentation using Hexagonal Convolutional Neural Networks

This paper introduces Hex U-Net, an innovative segmentation frameworkdesigned for hexagonal pixel-based images. It overcomes the shortcomingsof conventional methods that rely on rectangular pixel-based representations, byleveraging a hexagonal tessellation structure to better capture the complexitiesinherent in medical images. By harnessing recent advancements in pre-processingtechniques and convolutional neural network architectures, the study systematicallyexplores the integration of hexagonal grid structures into deep learningframeworks, with the objective of enhancing algorithmic performance anddice score in medical image segmentation tasks. The implementation process involvesa series of steps, including dataset pre-processing to convert the imagedata into hexagonal pixel-based format, the introduction of hexagonal convolutionkernel, and development of the Hex U-Net architecture. Experimental resultsdemonstrate significant improvements in segmentation accuracy, with Hex UNetachieving an impressive dice score of 0.873 compared to conventional architecturessuch as U-Net, VGG16, and ResNet18. The superior performance of HexU-Net underscores the efficacy of hexagonal pixel-based representations and specialisedconvolutional operations in capturing intricate spatial relationshipswithin medical images, thereby offering promising prospects for precise diagnosisand treatment planning in clinical practice