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- Title
Compensation of small data with large filters for accurate liver vessel segmentation from contrast-enhanced CT images.
- Authors
Chen, Wen; Zhao, Liang; Bian, Rongrong; Li, Qingzhou; Zhao, Xueting; Zhang, Ming
- Abstract
Background: Segmenting liver vessels from contrast-enhanced computed tomography images is essential for diagnosing liver diseases, planning surgeries and delivering radiotherapy. Nevertheless, identifying vessels is a challenging task due to the tiny cross-sectional areas occupied by vessels, which has posed great challenges for vessel segmentation, such as limited features to be learned and difficult to construct high-quality as well as large-volume data. Methods: We present an approach that only requires a few labeled vessels but delivers significantly improved results. Our model starts with vessel enhancement by fading out liver intensity and generates candidate vessels by a classifier fed with a large number of image filters. Afterwards, the initial segmentation is refined using Markov random fields. Results: In experiments on the well-known dataset 3D-IRCADb, the averaged Dice coefficient is lifted to 0.63, and the mean sensitivity is increased to 0.71. These results are significantly better than those obtained from existing machine-learning approaches and comparable to those generated from deep-learning models. Conclusion: Sophisticated integration of a large number of filters is able to pinpoint effective features from liver images that are sufficient to distinguish vessels from other liver tissues under a scarcity of large-volume labeled data. The study can shed light on medical image segmentation, especially for those without sufficient data.
- Subjects
COMPUTED tomography; MARKOV random fields; LIVER; IMAGE segmentation; IMAGE enhancement (Imaging systems); MACHINE learning; LIVER surgery
- Publication
BMC Medical Imaging, 2024, Vol 24, Issue 1, p1
- ISSN
1471-2342
- Publication type
Article
- DOI
10.1186/s12880-024-01309-1