Validation of a Semiautomated Liver Segmentation Method Using CT for Accurate Volumetry

Rationale and Objectives

To compare the repeatability and agreement of a semiautomated liver segmentation method with manual segmentation for assessment of total liver volume on CT (computed tomography).

Materials and Methods

This retrospective, institutional review board–approved study was conducted in 41 subjects who underwent liver CT for preoperative planning. The major pathologies encountered were colorectal cancer metastases, benign liver lesions and hepatocellular carcinoma. This semiautomated segmentation method is based on variational interpolation and 3D minimal path–surface segmentation. Total and subsegmental liver volumes were segmented from contrast-enhanced CT images in venous phase. Two image analysts independently performed semiautomated segmentations and two other image analysts performed manual segmentations. Repeatability and agreement of both methods were evaluated with intraclass correlation coefficients (ICC) and Bland–Altman analysis. Interaction time was recorded for both methods.

Results

Bland–Altman analysis revealed an intrareader agreement of −1 ± 27 mL (mean ± 1.96 standard deviation) with ICC of 0.999 ( P < .001) for manual segmentation and 12 ± 97 mL with ICC of 0.991 ( P < .001) for semiautomated segmentation. Bland–Altman analysis revealed an interreader agreement of −4 ± 22 mL with ICC of 0.999 ( P < .001) for manual segmentation and 5 ± 98 mL with ICC of 0.991 ( P < .001) for semiautomated segmentation. Intermethod agreement was found to be 3 ± 120 mL with ICC of 0.988 ( P < .001). Mean interaction time was 34.3 ± 16.7 minutes for the manual method and 8.0 ± 1.2 minutes for the semiautomated method ( P < .001).

Conclusions

A semiautomated segmentation method can substantially shorten interaction time while preserving a high repeatability and agreement with manual segmentation.

Assessment of liver volume is a mandatory step before extended hepatectomy for determining the anticipated future liver remnant and before living-donor liver transplantation for selection of appropriate candidates . Liver volumetry requires a multiplanar imaging modality. Computed tomography (CT) is currently the preferred imaging modality for surgical planning because of its superior spatial resolution and short acquisition time . Use of CT in presurgical imaging allows for concomitant assessment of vascular anatomy and quality of liver parenchyma and allows determination of total and lobar volume .

The reference standard method to estimate liver volume involves manually delineating the liver outline, a process called “segmentation,” on consecutive CT images. This method is cumbersome, time-consuming, and impractical for widespread clinical use . Formula-based liver volume estimation using patient height and weight has also been proposed . However, this approach is based on a linear regression equation and is not specific to patient anatomy .

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Materials and methods

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Study Subjects

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Table 1

Subject Demographics

Characteristic Data Total subjects, N (%) 41 (100) Sex Male (%) 22 (54) Female (%) 19 (46) Age (years) Mean ± standard deviation 55 ± 13 Body mass index in adults (kg/m 2 ) Mean ± standard deviation 26 ± 5 Pathologies Colorectal metastases 27 (66) Benign liver lesions 5 (12) Hepatocellular carcinoma 4 (10) Cholangiocarcinoma 2 (5) Biliary trauma 1 (2) Cystadenocarcinoma 1 (2) Cholangitis 1 (2)

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CT Imaging Technique

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Study Workflow

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Manual Segmentation

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Semiautomated Segmentation and Subsegmentation

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Table 2

Whole and Segmental Liver Volumes by Readers

Reader 1 (Manual) 3 (Semiautomated)P Value ∗ Whole-liver volume (mL) † 1689 ± 478 1688 ± 497 .92 Reader 1 2P Value ∗ Segmental volume (mL) † I 41 ± 16 53 ± 37 .01 II 204 ± 110 186 ± 77 .31 III 97 ± 66 74 ± 57 .05 IVa 186 ± 77 205 ± 83 .18 IVb 84 ± 54 101 ± 87 .10 V 292 ± 99 278 ± 116 .26 VI 221 ± 110 202 ± 114 .14 VII 278 ± 106 292 ± 132 .24 VIII 292 ± 103 306 ± 121 .23

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Statistical Analysis

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Results

Volumes

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Variability

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Table 3

Intrareader Repeatability, Interreader and Intermethod Agreement

Comparison Readers ICC Bland–Altman (mL) ∗ Repeatability on whole-liver volume Intrareader manual 1 vs. 1 0.999 −1 ± 27 (−28, 26) 2 vs. 2 1.000 −6 ± 11 (−17, 6) Intrareader semiautomated 3 vs. 3 0.995 −3 ± 67 (−70, 64) 4 vs. 4 0.991 12 ± 97 (−85, 109) Agreement on whole-liver volume Interreader 1 vs. 2 0.999 −4 ± 22 (−27, 18) 3 vs. 4 0.991 5 ± 98 (−93, 103) Intermethod † 1 vs. 3 0.992 −2 ± 93 (−95, 91) 1 vs. 4 0.988 3 ± 120 (−117, 124) Agreement on segmental volumes Interreader Segment I 1 vs. 2 0.585 12 ± 59 (−47, 71) Segment II 1 vs. 2 0.399 −17 ± 207 (−224, 190) Segment III 1 vs. 2 0.331 −23 ± 139 (−162, 116) Segment IVa 1 vs. 2 0.458 18 ± 164 (146, 182) Segment IVb 1 vs. 2 0.713 16 ± 121 (−105, 181) Segment V 1 vs. 2 0.758 −14 ± 150 (−164, 136) Segment VI 1 vs. 2 0.728 −20 ± 162 (−182, 142) Segment VII 1 vs. 2 0.831 14 ± 144 (−130, 158) Segment VIII 1 vs. 2 0.812 14 ± 139 (−125, 153)

ICC, intraclass correlation coefficient; SD, standard deviation.

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Repeatability

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Agreement

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Subgroup Analysis in Patients with HCC

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Error Measures

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Table 4

Segmentation Performance Measures

Error Measure Ideal Value Intrareader Manual (R1–R1′) ∗ Intrareader Semiautomated (R3–R3′) ∗ Intermethod (R1–R3) Volumetric overlap error (%) 0 † 2.9 ± 0.8 4.4 ± 1.3 6.4 ± 1.4 Average symmetric surface distance (mm) 0 0.4 ± 0.1 0.7 ± 0.3 1.0 ± 0.2 Root mean square symmetric surface distance (mm) 0 0.9 ± 0.2 1.6 ± 0.5 1.8 ± 0.5 Maximum symmetric surface distance (mm) 0 11.8 ± 4.9 17.2 ± 5.2 17.0 ± 5.1

Results reported as mean ± standard deviation.

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Time

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Discussion

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Appendix 1 Segmentation performance measures

Volumetric Overlap Error

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VOE(A,M)=1−|A∩M||A∪M|×100% VOE

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Average Symmetric Surface Distance (ASD)

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ASD(A,M)=1|S(A)|−|S(M)|(∑sA∈S(A)d(sA,S(M))+∑sM∈S(M)d(sM,S(A))) ASD

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Root mean square (RMS) Symmetric Surface Distance

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RMS(A,M)=1|S(A)|−|S(M)|×(∑sA∈S(A)d2(sA,S(M))+∑sM∈S(M)d2(sM,S(A)))−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−√−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−√ RMS

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Maximum Symmetric Surface Distance (MSD)

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MSD(A,M)=max{maxsA∈S(A)d(sA,S(M)),maxsM∈S(M)d(sM,S(A))} MSD

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Validation of a Semiautomated Liver Segmentation Method Using CT for Accurate Volumetry

Rationale and Objectives

Materials and Methods

Results

Conclusions

Materials and methods

Study Subjects

CT Imaging Technique

Study Workflow

Manual Segmentation

Semiautomated Segmentation and Subsegmentation

Statistical Analysis

Results

Volumes

Variability

Repeatability

Agreement

Subgroup Analysis in Patients with HCC

Error Measures

Time

Discussion

Appendix 1

Segmentation performance measures

Volumetric Overlap Error

Average Symmetric Surface Distance (ASD)

Root mean square (RMS) Symmetric Surface Distance

Maximum Symmetric Surface Distance (MSD)

References

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