Automatic segmentation of liver region based on extracted blood vessels

Lung tissue biomechanics imaged with synchrotron phase contrast microtomography in live rats

The magnitude and distribution of strain imposed on the peripheral airspaces by mechanical ventilation at the microscopic level and the consequent deformations are unknown despite their importance for understanding the mechanisms occurring at the onset of ventilator-induced lung injury. Here a 4-Dimensional (3D + time) image acquisition and processing technique is developed to assess pulmonary acinar biomechanics at microscopic resolution. Synchrotron radiation phase contrast CT with an isotropic voxel size of 6 µm3 is applied in live anesthetized rats under controlled mechanical ventilation. Video animations of regional acinar and vascular strain are acquired in vivo. Maps of strain distribution due to positive-pressure breaths and cardiovascular activity in lung acini and blood vessels are derived based on CT images. Regional strain within the lung peripheral airspaces takes average values of 0.09 ± 0.02. Fitting the expression S = kVn, to the changes in peripheral airspace area (S) and volume (V) during a positive pressure breath yields an exponent n = 0.82 ± 0.03, suggesting predominant alveolar expansion rather than ductal expansion or alveolar recruitment. We conclude that this methodology can be used to assess acinar conformational changes during positive pressure breaths in intact peripheral lung airspaces.

New Technique for Automatic Segmentation of Blood Vessels in CT Scan Images of Liver Based on Optimized Fuzzy C-Means Method

Automatic segmentation of medical CT scan images is one of the most challenging fields in digital image processing. The goal of this paper is to discuss the automatic segmentation of CT scan images to detect and separate vessels in the liver. The segmentation of liver vessels is very important in the liver surgery planning and identifying the structure of vessels and their relationship to tumors. Fuzzy C-means (FCM) method has already been proposed for segmentation of liver vessels. Due to classical optimization process, this method suffers lack of sensitivity to the initial values of class centers and segmentation of local minima. In this article, a method based on FCM in conjunction with genetic algorithms (GA) is applied for segmentation of liver's blood vessels. This method was simulated and validated using 20 CT scan images of the liver. The results showed that the accuracy, sensitivity, specificity, and CPU time of new method in comparison with FCM algorithm reaching up to 91%, 83.62, 94.11%, and 27.17 were achieved, respectively. Moreover, selection of optimal and robust parameters in the initial step led to rapid convergence of the proposed method. The outcome of this research assists medical teams in estimating disease progress and selecting proper treatments.

Development of A User-Centered Virtual Liver Surgery Planning System

The present study is intended to develop a user-centered virtual liver surgery planning system called Dr. Liver which has clinical applicability and effectiveness to support liver surgery. Existing virtual surgery systems needs to be customized to liver surgery and improved for better usability and time efficiency. A use scenario of a virtual liver surgery planning system was established through literature review, benchmarking, and interviews with surgeons. Based on the use scenario, detailed liver surgery planning procedures were defined. The major functions of Dr. Liver include (1) extraction of the liver, vessels, and tumors from abdominal CT images, (2) estimation of the standard liver volume of a patient, (3) volumetry of the extracted liver, vessels, and tumors, (4) segmentation of the liver into 8 segments based on structures of the extracted portal and hepatic veins, and (5) support of surgery planning. Novel algorithms were developed and implemented into Dr. Liver for accuracy and time efficiency. Various user-friendly features such as a procedural interface of virtual liver surgery planning were integrated into Dr. Liver for better usability. Dr. Liver would be applied to safe and rational planning of liver surgery.

Automatic liver segmentation method featuring a novel filter for multiphase multidetector-row helical computed tomography

PURPOSE

To introduce an automatic liver segmentation method that includes a novel filter for multiphase multidetector-row helical computed tomography.

MATERIALS AND METHODS

We acquired 3-phase multidetector-row computed tomographic scans that included unenhanced, arterial, and portal phases. The liver was segmented using our novel adaptive linear prediction filter designed to reduce the difference between filter input and output values in the liver region and to increase these values outside the liver region.

RESULTS

The segmentation algorithm produced a mean dice similarity coefficient (DSC) value of 91.4%.

CONCLUSION

The application of our adaptive linear prediction filter was effective in automatically extracting liver regions.

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

RATIONALE AND OBJECTIVES

The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver surgery.

MATERIALS AND METHODS

A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the automatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features.

RESULTS

The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in the range 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically and manually segmented vessel sections, with an average difference of 0.06 mm(2). The average values of corresponding false-positive and false-negative ratios were 7.7% and 2.3%, respectively.

CONCLUSIONS

A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computed tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative resections.

Recent advances in liver imaging

Liver surgery remains a difficult challenge in which preoperative data analysis and strategy definition may play a significant role in the success of the procedure. Medical image processing led to a major improvement of patient care by guiding the surgical gesture. From this initial data, new technologies of virtual reality and augmented reality can increase the potential of such images. The 3D modeling of the liver of patients from their CT scan or MRI thus allows an improved surgical planning. Simulation allows the procedure to be simulated preoperatively and offers the opportunity to train the surgical gesture before carrying it out. These three preoperative steps can be used intraoperatively thanks to the development of augmented reality, which consists of superimposing the preoperative 3D modeling of the patient onto the real intraoperative view of the patient and his/her organs. Augmented reality provides surgeons with a transparent view of the patient. This facilitated the intraoperative identification of the vascular anatomy and the control of the segmental arteries and veins in liver surgery, thus preventing intraoperative bleeding. It can also offer guidance due to the virtual improvement of their real surgical tools, which are tracked in real-time during the procedure. During the surgical procedure, augmented reality, therefore, offers surgeons a transparent view of their patient, which will lead to the automation of the most complex maneuvers. The new ways of processing and analyzing liver images have dramatically changed the approach to liver surgery.

Liver segmentation by intensity analysis and anatomical information in multi-slice CT images

PURPOSE

Quantitative assessment and essentially segmentation of liver and its tumours are of clinical importance in various procedures such as diagnosis, treatment planning, and monitoring. Moreover, segmentation of liver is the basis of further processing such as visualization, liver resection planning, and liver shape analysis. In this paper, we propose an algorithm to estimate an initial liver boundary.

METHODS

The proposed method consists of four steps as follows: first, we compute statistical parameters of liver's intensity range, associated with a large cross-section of liver CT image, utilizing expectation maximization (EM) algorithm. Second, by automatic extraction of ribs and segmentation of the heart, we define a ROI to confine the liver region for the next operations. Third, we propose a double thresholding approach to divide the liver intensity range into two overlapping ranges. In this case, based on a decision table, we label an object as a liver candidate or disregard it from the rest of the procedures. Finally, we employ an anatomical based rule to finalize a candidate as a liver tissue. In this case, we propose a color-map transformation scheme to convert the liver gray images into color images. In this way, we attempt to visually differentiate the liver from its surrounding tissues.

RESULTS

We have evaluated the techniques in the presence of 14 randomly selected local datasets as well as all datasets from the MICCAI 2007 Grand Challenge workshop database. For the local datasets, the average overlap error and average volume difference were of values of 15.3 and 2.8%, respectively. In the case of the MICCAI datasets, the above values were estimated as 20.3 and -4.5%, respectively.

CONCLUSION

The results reveal that the proposed technique is feasible to perform consistent initial liver borders. The boundary might be then employed in an 'Active Contour' algorithm to finalize the liver mask.

Fully automatic liver segmentation through graph-cut technique

The accurate knowledge of the liver structure including blood vessels topography, liver surface and lesion localizations is usually required in treatments like liver ablations and radiotherapy. In this paper, we propose an approach for automatic segmentation of liver complex geometries. It consists of applying a graph-cut method initialized by an adaptive threshold. The algorithm has been tested on 10 datasets (CT and MR). A parametric comparison with the results obtained by previous algorithms based on active contour is also carried out and discussed. Main limitations of active contour approaches result to be overcome and segmentation is improved. Feasibility to routinely use graph-cut approach for automatic liver segmentation is also demonstrated.

A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans

Accurate knowledge of the liver structure, including liver surface and lesion localization, is usually required in treatments such as liver tumor ablations and/or radiotherapy. This paper presents a new method and corresponding algorithm for fast segmentation of the liver and its internal lesions from CT scans. No interaction between the user and analysis system is required for initialization since the algorithm is fully automatic. A statistical model-based approach was created to distinguish hepatic tissue from other abdominal organs. It was combined to an active contour technique using gradient vector flow in order to obtain a smoother and more natural liver surface segmentation. Thereafter, automatic classification was performed to isolate hepatic lesions from liver parenchyma. Twenty-one datasets, presenting different anatomical and pathological situations, have been processed and analyzed. Special focus has been driven to the resulting processing time together with quality assessment. Our method allowed robust and efficient liver and lesion segmentations very close to the ground truth, in a relatively short processing time (average of 11.4 s for a 512 x 512-pixel slice). A volume overlap of 94.2% and an accuracy of 3.7 mm were achieved for liver surface segmentation. Sensitivity and specificity for tumor lesion detection were 82.6% and 87.5%, respectively.