Automatic Pancreas Segmentation using A Novel Modified Semantic Deep Learning Bottom-Up Approach

Abstract

Sharpe and smooth pancreas segmentation is a crucial and arduous problem in medical image analysis and investigation. A semantic deep learning bottom-up approach is the most popular and efficient method used for pancreas segmentation with a smooth and sharp result. The Automatic pancreas segmentation process is performed through semantic segmentation for abdominal computed tomography (CT) clinical images. A novel semantic segmentation is applied for acute pancreas segmentation with different angles of CT images. In the novel modified semantic approach, 12 layers are used. The proposed model is executed on a dataset of 80 patient single-phase CT images. For training purposes, 699 images and testing purposes 150 images are taken from a dataset with a different angle. The Proposed approach is used for many organs segmentation from CT scans clinical images with high accuracy. “transposedConv2dLayer” layer is used for up-sampling and down-sampling so the computation time period is reduced as related to the state-of-art. Bfscore, Dice Coefficient, Jaccard Coefficient are used to calculate similarity index values between test image and expected output image only. The proposed approach achieved a dice similarity index score upto 81±7.43%. The Class balancing process is executed with the help of class weight and data augmentation. In novel modified semantic segmentation, max-pooling layer, RELU layer, softmax layer, transposed conv2d layer and dicePixelClassification layer are used. DicePixelClassification is newly introduced and incorporated in a novel method for improved results. VGG-16, VGG-19 and RSnet-18 deep learning models are used for pancreas segmentation.