3D Hippocampus Segmentation Using a Hog Based Loss Function with Majority Pooling

Hippocampus (HC) segmentation plays a key role in diagnosis of predominant neuro-degenerative diseases like Alzheimer's, Parkinson's and common neurological disorders like Epilepsy. In this paper, we propose a solution to the 3D HC segmentation problem from the MRI data using shape driven...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 2260 - 2264
Main Authors De, Arijit, Tiwari, Mona, Chowdhury, Ananda S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Attention Unet
Feature extraction
Hippocampus Segmentation
Histograms
HOG features
Image segmentation
Loss function
Majority Pooling
Shape
Solid modeling
Three-dimensional displays
Throughput
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Summary:Hippocampus (HC) segmentation plays a key role in diagnosis of predominant neuro-degenerative diseases like Alzheimer's, Parkinson's and common neurological disorders like Epilepsy. In this paper, we propose a solution to the 3D HC segmentation problem from the MRI data using shape driven loss function and attention Unet. In particular, a Histogram of Oriented Gradients (HOG) based formulation is developed to extract shape features. We suggest a pooling technique as a substitute to the histogram calculation for HOG. This is to address the problem that histogram is not derivable thereby making the error in a loss function from histogram unsuitable for back propagation in deep learning models. The performance of our proposed model is validated on two publicly available datasets, namely, HarP and Kulaga-Yoskovitz (KY). Our segmentation accuracy with a dice similarity score of 0.947 and 0.923 in HarP and KY respectively is found to outperform the attention UNet model with only Dice loss, and, a number of state-of-the-art approaches.
DOI:10.1109/ICIP49359.2023.10223145