Pancreas Segmentation via Image Processing and Machine Learning – UROP Spring Symposium 2021

Pancreas Segmentation via Image Processing and Machine Learning

Agam Kohli

Agam Kohli

Pronouns: he/him/his

Research Mentor(s): Reza Soroushmehr, Research Investigator
Research Mentor School/College/Department: Department of Computational Medicine & Bioinformatics, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 1 (10am-10:50am)
Breakout Room: Room 16
Presenter: 6

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Pancreas segmentation is consistently one of the least accurate out of all organ segmentation due to its low contrast in its boundary and high anatomical variability in its geometric properties. Many hospitals and biomedical laboratories therefore do not have accurate methodologies for cancer detection, 3D modeling, etcetera for pancreas scans. This paper serves to improve current pancreas segmentation methods by making use of a type of Convolutional Neural Network (CNN) called a U-Net in addition to image processing techniques such as an Integrated Hausdorff-Sine Loss Function for preprocessing and 3D Gaussian smoothing for post processing. The CNN was trained on Computed Tomography (CT) images in n-fold cross-validation from a public NIH dataset and a private dataset from Michigan Medicine. Data augmentation was performed on the CT scans using Keras, an open-source neural network library. Accuracy and precision of the CNN was tested using a Dice-Sørensen Similarity Coefficient (DSC) and Jaccard Index (JI). The mean DSC and JI achieved for the proposed methods are expected to be at least 70.00%.

Authors: Reza Soroushmehr, Agam Kohli
Research Method: Computer Programming

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