Article detail · 2020
Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors
Journal
Diagnostic and Interventional Radiology- Year
- 2020
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue Diagnostic and Interventional Radiology
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
PURPOSE: To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging. METHODS: A total of 12 (6 semi-, 6 full-automatic) methods are evaluated. The semi-automatic segmentation algorithms are based on both traditional iterative models including watershed, fast marching, region growing, active contours and modern techniques including robust statistical segmenter and super-pixels. These methods entail some sort of interaction mechanism such as placing initialization seeds on images or determining a parameter range. The automatic methods are based on deep learning and they include three framework templates (DeepMedic, NiftyNet and U-Net) the first two of which are applied with default parameter sets and the last two involve adapted novel model designs. For 20 living donors (6 training and 12 test datasets), a group of imaging scientists and radiologists created ground truths by performing manual segmentations on contrast material-enhanced CT images. Each segmentation is evaluated using five metrics (i.e. volume overlap and relative volume errors, average/RMS/maximum symmetrical surface distances). The results are mapped to a scoring system and a final grade is calculated by taking their average. Accuracy and repeatability were evaluated using slice by slice comparisons and volumetric analysis. Diversity and complementarity are observed through heatmaps. Majority voting and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithms are utilized to obtain the fusion of the individual results. RESULTS: The top four methods are determined to be automatic deep models having 79.63, 79.46 and 77.15 and 74.50 scores. Intra-user score is determined as 95.14. Overall, deep automatic segmentation outperformed interactive techniques on all metrics. The mean volume of liver of ground truth is found to be 1409.93 mL ± 271.28 mL, while it is calculated as 1342.21 mL ± 231.24 mL using automatic and 1201.26 mL ± 258.13 mL using interactive methods, showing higher accuracy and less variation on behalf of automatic methods. The qualitative analysis of segmentation results showed significant diversity and complementarity enabling the idea of using ensembles to obtain superior results. The fusion of automatic methods reached 83.87 with majority voting and 86.20 using STAPLE that are only slightly less than fusion of all methods that achieved 86.70 (majority voting) and 88.74 (STAPLE). CONCLUSION: Use of the new deep learning based automatic segmentation algorithms substantially increases the accuracy and repeatability for segmentation and volumetric measurements of liver. Fusion of automatic methods based on ensemble approaches exhibits best results almost without any additional time cost due to potential parallel execution of multiple models.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
78 citations
OpenAlex cited_by_count (cache / database)
10 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- CHAOS Challenge- combined (CT-MR) healthy abdominal organ segmentation 2021
- CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation 2021
- Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks 2021
- Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks 2021
- Comparison of Deep Learning Models in Carotid Artery Intima-Media Thickness Ultrasound Images: CAIMTUSNet 2022
- Cross-Modal Learning via Adversarial Loss and Covariate Shift for Enhanced Liver Segmentation 2024
- Cross-Modal Learning via Adversarial Loss and Covariate Shift for Enhanced Liver Segmentation 2024
- Chain code strategy for lossless storage and transfer of segmented binary medical data 2023
- Chain code strategy for lossless storage and transfer of segmented binary medical data 2023
- Comparison of Manual, Semi-Automatic, and Automatic CT-Based Methods for Liver Volume Segmentation 2026