Evaluation of Preprocessing Techniques for U-Net Based Automated Liver Segmentation
This work addresses liver segmentation for medical imaging analysis, but it is incremental as it focuses on optimizing preprocessing steps for an existing deep learning method.
The paper tackled the problem of automated liver segmentation from CT images by evaluating preprocessing techniques for U-Net, finding that a combination of HU-windowing, median filtering, and z-score normalization achieved optimal performance with Dice coefficients of 96.93% (training), 90.77% (validation), and 90.84% (testing).
To extract liver from medical images is a challenging task due to similar intensity values of liver with adjacent organs, various contrast levels, various noise associated with medical images and irregular shape of liver. To address these issues, it is important to preprocess the medical images, i.e., computerized tomography (CT) and magnetic resonance imaging (MRI) data prior to liver analysis and quantification. This paper investigates the impact of permutation of various preprocessing techniques for CT images, on the automated liver segmentation using deep learning, i.e., U-Net architecture. The study focuses on Hounsfield Unit (HU) windowing, contrast limited adaptive histogram equalization (CLAHE), z-score normalization, median filtering and Block-Matching and 3D (BM3D) filtering. The segmented results show that combination of three techniques; HU-windowing, median filtering and z-score normalization achieve optimal performance with Dice coefficient of 96.93%, 90.77% and 90.84% for training, validation and testing respectively.