An Automatic Level Set Based Liver Segmentation from MRI Data Sets

A fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results

The effect of using different embolic agents on survival in transarterial chemoembolization of hepatocellular carcinoma: Gelfoam versus polyvinyl alcohol

PURPOSE We aimed to compare the effect of using different embolic agents such as gelfoam and polyvinyl alcohol (PVA) on survival, tumor response, and complications in transarterial chemoembolization (TACE) of hepatocellular carcinoma (HCC) patients. MATERIALS AND METHODS We retrospectively reviewed the medical records of 38 inoperable HCC patients who underwent TACE between August 1998 and April 2007. A total of 50 TACE sessions were performed using PVA (n=18) or gelfoam particles (n=20), following the application of 60 mg doxorubicin with 10-20 mL lipiodol emulsion. The PVA and gelfoam groups were compared based on clinical, laboratory, and demographic variables. Survival rates were calculated starting from the first TACE session using the Kaplan-Meier analysis. RESULTS There was no significant difference between the survival rates of PVA and gelfoam groups (P = 0.235). Overall survival rates at 12, 24, 36, 48, and 60 months were 55%, 36%, 15%, 7%, and 5%, respectively. Tumor response, age, lipiodol accumulation type, number of HCC foci, complications, and serum alpha-fetoprotein level were significant factors for survival in all patients. CONCLUSION Use of gelfoam or PVA as the embolic agent did not have a significant impact on survival. Complete tumor response, intensive lipiodol accumulation in tumor, older age (>60 years), fewer (≤3) HCC foci, and low serum alpha-fetoprotein level (≤400 ng/mL) were found to improve cumulative survival significantly. © Turkish Society of Radiology 2014

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors

PURPOSE We aimed to compare the accuracy and repeatability of emerging machine learning-based (i.e., deep learning) automatic segmentation algorithms with those of well-established interactive semi-automatic methods for determining liver volume in living liver transplant donors at computed tomography (CT) imaging. METHODS A total of 12 methods (6 semi-automatic, 6 full-automatic) were evaluated. The semi-automatic segmentation algorithms were based on both traditional iterative models including watershed, fast marching, region growing, active contours arid modern techniques including robust statistics segmenter and super-pixels. These methods entailed some sort of interaction mechanism such as placing initialization seeds on images or determining a parameter range. The automatic methods were based on deep learning and included three framework templates (DeepMedic, NiftyNet and U-Net), the first two of which were applied with default parameter sets and the last two involved adapted novel model designs. For 20 living donors (8 training and 12 test datasets), a group of imaging scientists and radiologists created ground truths by performing manual segmentations on contrast-enhanced CT images. Each segmentation was evaluated using five metrics (i.e., volume overlap and relative volume errors, average/root-mean-square/maximum symmetrical surface distances). The results were mapped to a scoring system and a final grade was calculated by taking their average. Accuracy and repeatability were evaluated using slice-by-slice comparisons and volumetric analysis. Diversity and complementarily were observed through heatmaps. Majority voting (MV) and simultaneous truth and performance level estimation (STAPLE) algorithms were utilized to obtain the fusion of the individual results. RESULTS The top four methods were automatic deep learning models, with scores of 79.63, 79.46, 77.15, and 74.50. Intra-user score was determined as 95.14. Overall, automatic deep learning segmentation outperformed interactive techniques on all metrics. The mean volume of liver of ground truth was 1409.93 +/- 271.28 mL, while it was calculated as 1342.21 +/- 231.24 mL using automatic and 1201.26 +/- 258.13 mL using interactive methods, showing higher accuracy and less variation with 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 score of automatic methods reached 83.87 with MV and 86.20 with STAPLE, which my slightly less than fusion of all methods (MV, 86.70) and (STAPLE, 88.74). 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 with almost no additional time cost due to potential parallel execution of multiple models

Uterine Rupture by Intended Mode of Delivery in the UK: A National Case-Control Study

A case-control study using UK data estimates the risk of uterine rupture in subsequent deliveries amongst women who have had a previous caesarean section

Influence of dietary protein and sex on walking ability and bone parameters of broilers

WOS: 000073941200014PubMed ID: 96498801. The study was conducted to investigate the effect of dietary protein on the walking ability and bone parameters of broilers reared under summer temperatures which ranged from 26 degrees to 32 degrees C(+/- 2 degrees C). 2. Three different dietary protein combinations were used. The diets (per kg) were: low protein with 205 g crude protein and 12.94 MJ ME, 184 g crude protein and 12.75 MJ ME; medium protein with 219 g crude protein and 12.99 MJ ME, 201 g crude protein and 12.87 MJ ME; and high protein with 238 g crude protein and 12.99 MJ ME, 216 g crude protein and 12.96 MJ ME from 0 to 4 and 4 to 7 weeks of age, respectively. Body weights of birds were recorded and birds' walking ability (gait scoring) were scored for each bird, according to 3 categories (completely normal to immobile, at 4 and 7 weeks). Tibia parameters and tibia plateau angles were also determined at 7 weeks. 3. Birds fed on the low protein were lighter than those fed on the medium or high protein diets. At 7 weeks, birds with poor walking ability weighed 149 g less than birds with no walking difficulty. 4. Bone parameters were not affected by dietary protein, sex or gait score. There was a significantly positive correlation between bone strength and radiographic density. Bone strength was also significantly correlated with bone weight and length

Fully automated liver segmentation from SPIR image series.

Accurate liver segmentation is an important component of surgery planning for liver transplantation, which enables patients with liver disease a chance to survive. Spectral pre-saturation inversion recovery (SPIR) image sequences are useful for liver vessel segmentation because vascular structures in the liver are clearly visible in these sequences. Although level-set based segmentation techniques are frequently used in liver segmentation due to their flexibility to adapt to different problems by incorporating prior knowledge, the need to initialize the contours on each slice is a common drawback of such techniques. In this paper, we present a fully automated variational level set approach for liver segmentation from SPIR image sequences. Our approach is designed to be efficient while achieving high accuracy. The efficiency is achieved by (1) automatically defining an initial contour for each slice, and (2) automatically computing weight values of each term in the applied energy functional at each iteration during evolution. Automated detection and exclusion of spurious structures (e.g. cysts and other bright white regions on the skin) in the pre-processing stage increases the accuracy and robustness. We also present a novel approach to reduce computational cost by employing binary regularization of level set function. A signed pressure force function controls the evolution of the active contour. The method was applied to ten data sets. In each image, the performance of the algorithm was measured using the receiver operating characteristics method in terms of accuracy, sensitivity and specificity. The accuracy of the proposed method was 96%. Quantitative analyses of results indicate that the proposed method can accurately, efficiently and consistently segment liver images