An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data

Background

To estimate median liver iron concentration (LIC) calculated from magnetic resonance imaging, excluded vessels of the liver parenchyma region were defined manually. Previous works proposed the automated method for excluding vessels from the liver region. However, only user-defined liver region remained a manual process. Therefore, this work aimed to develop an automated liver region segmentation technique to automate the whole process of median LIC calculation.

Methods

553 MR examinations from 471 thalassemia major patients were used in this study. LIC maps (in mg/g dry weight) were calculated and used as the input of segmentation procedures. Anatomical landmark data were detected and used to restrict ROI. After that, the liver region was segmented using fuzzy c-means clustering and reduced segmentation errors by morphological processes. According to the clinical application, erosion with a suitable size of the structuring element was applied to reduce the segmented liver region to avoid uncertainty around the edge of the liver. The segmentation results were evaluated by comparing with manual segmentation performed by a board-certified radiologist.

Results

The proposed method was able to produce a good grade output in approximately 81% of all data. Approximately 11% of all data required an easy modification step. The rest of the output, approximately 8%, was an unsuccessful grade and required manual intervention by a user. For the evaluation matrices, percent dice similarity coefficient (%DSC) was in the range 86–92, percent Jaccard index (%JC) was 78–86, and Hausdorff distance (H) was 14–28 mm, respectively. In this study, percent false positive (%FP) and percent false negative (%FN) were applied to evaluate under- and over-segmentation that other evaluation matrices could not handle. The average of operation times could be reduced from 10 s per case using traditional method, to 1.5 s per case using our proposed method.

Conclusion

The experimental results showed that the proposed method provided an effective automated liver segmentation technique, which can be applied clinically for automated median LIC calculation in thalassemia major patients.

Evaluation of hepatic iron concentration heterogeneities using the MRI R2* mapping method

OBJECTIVE

To measure hepatic iron concentration (HIC) heterogeneities using a magnetic resonance R2* mapping method.

PATIENTS AND METHODS

Ninety-four patients with suspected hepatic iron overload and 10 volunteers were included prospectively. A multi-echo R2* sequence with fat saturation and with three post-processing fitting methods (a single exponential decay model with or without truncation, SED and SEDt, and a constant offset model, COS) was compared to a signal intensity ratio method (SIR), considered as the reference. HIC heterogeneity was evaluated from R2* mapping after placing a ROI on each liver segment.

RESULTS

A strong linear correlation between SIR and R2* methods using the SEDt and COS models was observed (r = 0.973 and 0.955, respectively). Volunteers and patient liver variabilities, quantified by mean intra-liver standard deviation (SD) were 1.58 μmol/g (mean range 5.06 μmol/g) and 4.73 μmol/g (mean range 19.08 μmol/g), respectively. For the patient group, the highest HIC was observed in the IV^th segment. Heterogeneity increased for patients with an HIC > 60 μmol/g (mean intra-liver SD = 13.90 μmol/g; mean range = 50.60 μmol/g).

CONCLUSION

This study is the first to demonstrate in vivo HIC heterogeneities using whole-liver mapping analysis. These preliminary results require confirmation through further studies, but might be useful in cases of single ROI analysis.

The relationship between myocardial and hepatic T2 and T2* at 1.5T and 3T MRI in normal and iron-overloaded patients

Background Cardiac and liver iron assessment using magnetic resonance imaging (MRI) is non-invasive and used as a preclinical "endpoint" in asymptomatic patients and for serial iron measurements in iron-overloaded patients. Purpose To compare iron measurements between hepatic and myocardial T2* and T2 at 1.5T and 3T MRI in normal and iron-overloaded patients. Material and Methods The T2 and T2* values from the regions of interest (ROIs) at mid-left ventricle and mid-hepatic slices were evaluated by 1.5T and 3T MRI scans for healthy and iron-overloaded patients. Results For iron-overloaded patients, the myocardial T2 (1.5T) and myocardial T2 (3T) values were 60.3 ms (range = 56.2-64.8 ms) and 55 ms (range = 51.6-60.1 ms) (ρ = 0.3679) while the myocardial T2* (3T) 20.5 ms (range = 18.4-25.9 ms) was shorter than the myocardial T2* (1.5T) 35.9 ms (range = 31.4-39.5 ms) (ρ = 0.6454). The hepatic T2 at 1.5T and 3T were 19.1 ms (range = 14.8-27.9 ms) and 15.5 ms (14.6-20.4 ms) (ρ = 0.9444) and the hepatic T2* at 1.5T and 3T were 2.7 ms (range = 1.8-5.6 ms) and 1.8 ms (range = 1.1-2.9 ms) (ρ = 0.9826). The line of best fit exhibiting the linearity of the hepatic T2* (1.5T) and hepatic T2* (3T) had a slope of 2 and an intercept of -0.387 ms (R = 0.984). Conclusion Our study found myocardial T2 (1.5T) nearly equal to T2 (3T) with myocardial T2* (3T) 1.75 shorter than myocardial T2* (1.5T). The relationship of hepatic T2* (1.5T) and hepatic T2* (3T) was linear with T2* (1.5T) approximately double to T2* (3T) in iron-overloaded patients. This linear relationship between hepatic T2* (1.5T) and hepatic T2 (3T) could be an alternative method for estimating liver iron concentration (LIC) from 3T.

An Automated Segmentation of R2* Iron-Overloaded Liver Images Using a Fuzzy C-Mean Clustering Scheme

OBJECTIVES

The objectives of this study were to develop and test an automated segmentation of R2* iron-overloaded liver images using fuzzy c-mean (FCM) clustering and to evaluate the observer variations.

MATERIALS AND METHODS

Liver R2* images and liver iron concentration (LIC) maps of 660 thalassemia examinations were randomly separated into training (70%) and testing (30%) cohorts for development and evaluation purposes, respectively. Two-dimensional FCM used R2* images, and the LIC map was implemented to segment vessels from the parenchyma. Two automated FCM variables were investigated using new echo time and membership threshold selection criteria based on the FCM centroid distance and LIC levels, respectively. The new method was developed on a training cohort and compared with manual segmentation for segmentation accuracy and to a previous semiautomated method, and a semiautomated scheme was suggested to improve unsuccessful results. The automated variables found from the training cohort were assessed for their effectiveness in the testing cohort, both quantitatively and qualitatively (the latter by 2 abdominal radiologists using a grading method, with evaluations of observer variations). A segmentation error of less than 30% was considered to be a successful result in both cohorts, whereas, in the testing cohort, a good grade obtained from satisfactory automated results was considered a success.

RESULTS

The centroid distance method has a segmentation accuracy comparable with the previous-best, semiautomated method. About 94% and 90% of the examinations in the training and testing cohorts were automatically segmented out successfully, respectively. The failed examinations were successfully segmented out with thresholding adjustment (3% and 8%) or by using alternative results from the previous 1-dimensional FCM method (3% and 2%) in the training and testing cohorts, respectively. There were no failed segmentation examinations in either cohort. The intraobserver and interobserver variabilities were found to be in substantial agreement.

CONCLUSIONS

Our new method provided a robust automated segmentation outcome with a high ease of use for routine clinical application.

Prevalence and predictors of cardiac and liver iron overload in patients with thalassemia: A multicenter study based on real-world data

Prevalence of cardiac and liver iron overload in patients with thalassemia in real-world practice may vary among different regions especially in the era of widely-used iron chelation therapy. The aim of this study was to determine the prevalence of cardiac and liver iron overload in and the management patterns of patients with thalassemia in real-world practice in Thailand. We established a multicenter registry for patients with thalassemia who underwent magnetic resonance imaging (MRI) as part of their clinical evaluation. All enrolled patients underwent cardiac and liver MRI for assessment of iron overload. There were a total of 405 patients enrolled in this study. The mean age of patients was 18.8±12.5years and 46.7% were male. Two hundred ninety-six (73.1%) of patients received regular blood transfusion. Prevalence of cardiac iron overload (CIO) and liver iron overload (LIO) was 5.2% and 56.8%, respectively. Independent predictors for iron overload from laboratory information were serum ferritin and transaminase for both CIO and LIO. Serum ferritin can be used as a screening tool to rule-out CIO and to diagnose LIO. Iron chelation therapy was given in 74.6%; 15.3% as a combination therapy.

Improved R2* liver iron concentration assessment using a novel fuzzy c-mean clustering scheme

BACKGROUND

In thalassemia patients, R2* liver iron concentration (LIC) measurement is a common clinical tool for assessing iron overload and for determining necessary chelator dose and evaluating its efficacy. Despite the importance of accurate LIC measurement, existing methods suffer from LIC variability, especially at the severe iron overload range due to inclusion of vessel parts in LIC calculation. In this study, we build upon previous Fuzzy C-Mean (FCM) clustering work to formulate a scheme with superior performance in segmenting vessel pixels from the parenchyma. Our method (MIX-FCM) combines our novel 2D-FCM with the existing 1D-FCM algorithm. This study further assessed possible optimal clustering parameters (OP scheme) and proposed a semi-automatic (SA) scheme for routine clinical application.

METHODS

Segmentation of liver parenchyma and vessels was performed on T2* images and their LIC maps in 196 studies from 147 thalassemia major patients. We used manual segmentation as the reference. 1D-FCM clustering was performed on the acquired image alone and 2D-FCM used both the acquired image and its LIC data. To execute the MIX-FCM method, the best outcome (OP-MIX-FCM) was selected from the aforementioned methods and was compared to the SA-MIX-FCM scheme. We used the percent value of the normalized interquartile range (nIQR) to its median to evaluate the variability of all methods.

RESULTS

2D-FCM clustering is more effective than 1D-FCM clustering at the severe overload range only, but inferior for other ranges (where 1D-FCM provides suitable results). This complementary performance between the two methods allows MIX-FCM to improve results for all ranges. OP-MIX-FCM clustering error was 2.1 ± 2.3%, compared with 10.3 ± 9.9% and 7.0 ± 11.9% from 1D- and 2D-FCM clustering, respectively. SA-MIX-FCM result was comparable to OP-MIX-FCM result, with both schemes showing ability to decrease overall nIQR by approximately 30%.

CONCLUSION

Our proposed 2D-FCM algorithm is not as superior to 1D-FCM as hypothesized. In contrast, our MIX-FCM method benefits from the best of both methods to obtain the highest segmentation accuracy at all ranges. Moreover, segmentation accuracy of the practical scheme (SA-MIX-FCM) is comparable to segmentation accuracy of the reference scheme (OP-MIX-FCM). Finally, we confirmed that segmentation is crucial to improving LIC assessments, especially at the severe iron overload range.

Optimal region-of-interest MRI R2* measurements for the assessment of hepatic iron content in thalassaemia major

OBJECTIVES

To evaluate the performance of region-of-interest (ROI)-based MRI R2* measurements by using the first-moment noise-corrected model (M(1)NCM) to correct the non-central Chi noise in magnitude images from phased arrays for hepatic iron content (HIC) assessment.

METHODS

R2* values were quantified using the M(1)NCM model. Three approaches were employed to determine the representative R2*: fitting of the ROI-averaged signal (average-then-fit, ATF); outputting the median and mean of R2*s from the pixel-wise fitting of decay signals within the ROI (denoted as PWFmed and PWFmea, respectively). The accuracy and precision of the three approaches were evaluated on synthesized data. The agreement among these approaches and their intra- and inter-observer reproducibility were assessed on 105 thalassaemia major patients.

RESULTS

Simulations showed that ATF consistently yielded the highest accuracy and precision at varying noise levels. By contrast, PWFmed and PWFmea slightly and significantly overestimated high R2* at poor signal-to-noise ratios, respectively. Patient study showed that ATF agreed well with PWFmed, whereas PWFmea produced high R2* measurements for patients with severe HIC. No significant difference was observed in the reproducibility of the three approaches.

CONCLUSIONS

PWFmea tends to overestimate high R2*, whereas ATF and PWFmed can produce more accurate R2* measurements for HIC assessment.

Treating iron overload in patients with non-transfusion-dependent thalassemia

Despite receiving no or only occasional blood transfusions, patients with non-transfusion-dependent thalassemia (NTDT) have increased intestinal iron absorption and can accumulate iron to levels comparable with transfusion-dependent patients. This iron accumulation occurs more slowly in NTDT patients compared to transfusion-dependent thalassemia patients, and complications do not arise until later in life. It remains crucial for these patients' health to monitor and appropriately treat their iron burden. Based on recent data, including a randomized clinical trial on iron chelation in NTDT, a simple iron chelation treatment algorithm is presented to assist physicians with monitoring iron burden and initiating chelation therapy in this group of patients. Am. J. Hematol. 88:409–415, 2013. © 2013 Wiley Periodicals, Inc.