Effects of Enhancement on Deep Learning Based Hepatic Vessel Segmentation

• Published in: Electronics (Basel) 2021-01, Vol.10 (10), p.1165
• Main Authors: Survarachakan, Shanmugapriya, Pelanis, Egidijius, Khan, Zohaib Amjad, Kumar, Rahul Prasanna, Edwin, Bjørn, Lindseth, Frank
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Cancer; Datasets; Deep learning; Eigenvalues; Image analysis; Image filters; Image reconstruction; Image segmentation; Liver; Magnetic resonance imaging; Medical imaging; Metastasis; Morphology; Noise; Preprocessing; Qualitative analysis; Three dimensional models
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics10101165

Colorectal cancer (CRC) is the third most common type of cancer with the liver being the most common site for cancer spread. A precise understanding of patient liver anatomy and pathology, as well as surgical planning based on that, plays a critical role in the treatment process. In some cases, surgeons request a 3D reconstruction, which requires a thorough analysis of the available images to be converted into 3D models of relevant objects through a segmentation process. Liver vessel segmentation is challenging due to the large variations in size and directions of the vessel structures as well as difficult contrasting conditions. In recent years, deep learning-based methods had been outperforming the conventional image analysis methods in the field of medical imaging. Though Convolutional Neural Networks (CNN) have been proved to be efficient for the task of medical image segmentation, the way of handling the image data and the preprocessing techniques play an important role in segmentation. Our work focuses on the combination of different vesselness enhancement filters and preprocessing methods to enhance the hepatic vessels prior to segmentation. In the first experiment, the effect of enhancement using individual vesselness filters was studied. In the second experiment, the effect of gamma correction on vesselness filters was studied. Lastly, the effect of fused vesselness filters over individual filters was studied. The methods were evaluated on clinical CT data. The quantitative analysis of the results in terms of different evaluation metrics from experiments can be summed up as (i) each of the filtered methods shows an improvement as compared to unenhanced with the best mean DICE score of 0.800 in comparison to 0.740 for unenhanced; (ii) applied gamma correction provides a statistically significant improvement in the performance of each filter with improvement in mean DICE of around 2%; (iii) both the fused filtered images and fused segmentation give the best results (mean DICE score of 0.818 and 0.830, respectively) with the statistically significant improvement compared to the individual filters with and without Gamma correction. The results have further been verified by qualitative analysis and hence show the importance of our proposed fused filter and segmentation approaches.