Evaluation of Preprocessing Techniques for U-Net Based Automated Liver Segmentation
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 33 references that are in the map. First posted on arXiv 26 Mar 2021.
Builds on
12 of 33 references.
References · 12
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.