Automated segmentation of the portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial markers and vasculature stents. In this paper, we propose a novel framework for automated segmentation of the PV from computed tomography (CT) images. We apply convolutional neural networks (CNNs) to learn the consistent appearance patterns of the PV using a training set of CT images with reference annotations and then enhance the PV in previously unseen CT images. Markov random fields (MRFs) were further used to smooth the results of the enhancement of the CNN enhancement and remove isolated mis-segmented regions. Finally, CNN-MRF-based enhancement was augmented with PV centerline detection that relied on PV anatomical properties such as tubularity and branch composition. The framework was validated on a clinical database with 72 CT images of patients scheduled for liver stereotactic body radiation therapy. The obtained accuracy of the segmentation was [Formula: see text] 0.83 and [Formula: see text] 1.08 mm in terms of the median Dice coefficient and mean symmetric surface distance, respectively, when segmentation is encompassed into the PV region of interest. The obtained results indicate that CNNs and anatomical analysis can be used for the accurate segmentation of the PV and potentially integrated into liver radiation therapy planning.

Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning.

Hepatic vessel segmentation for 3D planning of liver surgery experimental evaluation of a new fully automatic algorithm.

Liver vessel segmentation in CTA images is a challenging task, especially in the case of noisy images. This paper investigates whether pre-filtering improves liver vessel segmentation in 3D CTA images. We introduce a quantitative evaluation of several well-known filters based on a proposed liver vessel segmentation method on CTA images. We compare the effect of different diffusion techniques i.e. Regularized Perona-Malik, Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion as well as the vesselness approaches proposed by Sato, Frangi and Erdt. Liver vessel segmentation of the pre-processed images is performed using a histogram-based region grown with local maxima as seed points. Quantitative measurements (sensitivity, specificity and accuracy) are determined based on manual landmarks inside and outside the vessels, followed by T-tests for statistic comparisons on 51 clinical CTA images. The evaluation demonstrates that all the filters make liver vessel segmentation have a significantly higher accuracy than without using a filter (p < 0.05); Hybrid Diffusion with Continuous Switch achieves the best performance. Compared to the diffusion filters, vesselness filters have a greater sensitivity but less specificity. In addition, the proposed liver vessel segmentation method with pre-filtering is shown to perform robustly on a clinical dataset having a low contrast-to-noise of up to 3 (dB). The results indicate that the pre-filtering step significantly improves liver vessel segmentation on 3D CTA images.

https://iopscience.iop.org/article/10.1088/0031-9155/60/10/3905/pdf

Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images.

The advancements in basic sciences and the availability of sophisticated technological aids to surgical removal of gliomas have led over the last few years to the rise of innovative surgical strategies, the identification of better prognostic/predictive biomolecular factors, and the development of novel drugs and all are meant to profoundly impact the outcome of patients diagnosed with these aggressive tumours. Unfortunately, the treatment protocols available nowadays still confer only a small survival advantage at a potentially high cost in terms of overall well-being. In this review we identified the potential and limits of the most promising research trends in the management of glioma patients, also highlighting the related externalities. Finally, we focused our attention on the imbalance between the technical and behavioral aspects pertinent to this research area, which ultimately represent the two sides of the same coin.

/pdf/management-of-gliomas-overview-of-the-latest-technological-176fbyd8nn.pdf

Management of Gliomas: Overview of the Latest Technological Advancements and Related Behavioral Drawbacks.

The liver vessels, which have low signal and run next to brighter bile ducts, are difficult to segment from MR images. This study presents a fully automated and adaptive method to segment portal and hepatic veins on magnetic resonance images. In the proposed approach, segmentation of these vessels is achieved in four stages: (i) initial segmentation, (ii) refinement, (iii) reconstruction, and (iv) post-processing. In the initial segmentation stage, k-means clustering is used, the results of which are refined iteratively with linear contrast stretching algorithm in the next stage, generating a mask image. In the reconstruction stage, vessel regions are reconstructed with the marker image from the first stage and the mask image from the second stage. Experimental data sets include slices that show fat tissues, which have the same gray level values with vessels, outside the margin of the liver. These structures are removed in the last stage. Results show that the proposed approach is more efficient than other thresholding-based methods. Copyright © 2016 John Wiley & Sons, Ltd.

Vessel segmentation from abdominal magnetic resonance images: adaptive and reconstructive approach.

The detection and classification of hepatic vessels in diagnostic images are essential for hepatic pre-surgery planning. Our team has developed a tool for classification, analysis, and 3D reconstruction of the hepatic and portal systems. Our software first extracts a graphic representation of a set of connected voxels, representing both systems. It then calculates two binary volumes representing the main part of the two venous systems. Finally, it combines these results to obtain the correct vessel classification. Segmentation steps are semi-automatic and require about 40 min to complete. Schematization and classification steps are automatic and require about 17 min for results. The software provides a correct and detailed reconstruction even where pathologies have caused morphological and geometrical variations in the vessels. The time required for the entire procedure is compatible with clinical requirements, providing an efficient tool for diagnosis and surgical planning.

L. Antiga

Papers

Software for hepatic vessel classification: feasibility study for virtual surgery.