Effect of Kernels Used for the Reconstruction of MDCT Datasets on the Semi-Automated Segmentation and Volumetry of Liver Lesions

Abstract

Purpose: To evaluate the effect of different reconstruction kernels on the semi-automated segmentation of liver lesions in MDCT.

Materials and Methods: A total 62 liver lesions were measured by three independent radiologists with the semi-automated segmentation software Oncology-Prototype (Fraunhofer MEVIS, Siemens Healthcare, Germany) using MDCT datasets (3-mm slice thickness, 2-mm increment) reconstructed with standard, soft and detailed kernels (Philips B, A and D). To ensure objective measurements, only lesions with satisfactory initial segmentation were included, and manual correction was not used. The effective diameter and volume were recorded for each lesion. Segmentation in the soft and detailed kernel datasets was performed by copying the initial seed's position from the standard kernel dataset.

Results: The mean effective lesion diameter was 19.9 ± 9.7 mm using the standard kernel. Comparing the three kernels, no significant differences were found. The mean difference was 1 % ± 6 % for the standard kernel compared to the soft kernel, 3 % ± 13 % for the standard kernel vs. the detailed kernel and 2 % ± 9 % for the soft kernel compared to the detailed kernel. The intra-class correlation coefficients were > 0.96 in all cases.

Conclusion: The semi-automated segmentation and volumetry of liver lesions shows reliable measurements regardless of the kernel used for reconstruction of the MDCT dataset.

Key Points:

• Semi-automated segmentation and volumetry of liver lesions is reliable regardless of the kernel used for reconstruction of the MDCT dataset.

• Until today the gold standard for the evaluation of tumor response has been unidimensional manual measurement.

• Volumetric measurements could improve the assessment of tumor growth.

Citation Format:

• Pinto dos Santos D, Klöckner R, Wunder K et al. Effect of Kernels Used for the Reconstruction of MDCT Datasets on the Semi-Automated Segmentation and Volumetry of Liver Lesions. Fortschr Röntgenstr 2014; 186: 780 – 784

Zusammenfassung

Ziel: Untersuchung der Auswirkungen verschiedener Rekonstruktionkernel auf die halbautomatische Segmentierung von Leberläsionen in der MDCT.

Material und Methoden: Insgesamt 62 Leberläsionen wurden durch drei unabhängige Radiologen mithilfe der Oncology-Prototype Segmentierungssoftware (Fraunhofer MEVIS, Siemens Healthcare, Germany) halbautomatisch vermessen. Die verwendeten CT-Datensätze waren jeweils mittels standard, soft und detailed (Philips B, A and D) Kernel rekonstruiert worden. Um eine objektive Messung sicherzustellen wurden nur Läsionen eingeschlossen, deren initiale Segmentierung zufriedenstellend war, manuelle Korrekturen wurden nicht vorgenommen. Effektiver Durchmesser und Volumen wurde für alle Läsionen erhoben. Die Segmentierung in den mittels soft und detailed Kernel rekonstruierten Datensätzen wurde durch Kopieren der Seedposition aus dem Standardkernel Datensatz vorgenommen.

Ergebnisse: Der mittlere effektive Läsionsdurchmesser betrug bei Verwendung des Standardkernels 19,9 ± 9,7 mm. Der Vergleich aller drei Kernel untereinander zeigte keine signifikanten Unterschiede. Der mittlere Unterschied zwischen standard und soft Kernel betrug 1 % ± 6 %, zwischen standard und detailed 3 % ± 13 % und zwischen soft und detailed 2 % ± 9 %. Die Intra-Klassen-Korrelationskoeffizienten lagen für alle Vergleiche bei > 0,96.

Schlussfolgerung: Die halbautomatische Segmentierung und Volumetrie von Leberläsionen zeigt verlässliche Messungen unabhängig von dem zur Rekonstruktion der MDCT-Datensätze verwandten Kernel.

Kernaussagen:

• Die halbautomatische Segmentierung und Volumetrie von Leberläsionen is verlässlich unabhängig von dem zur Rekonstruktion der MDCT-Datensätze verwandten Kernel.

• Bis heute ist der Gold-Standard zur Evaluation von Tumoransprechen die manuelle unidimensionale Messung.

• Volumetrische Messungen könnten die Beurteilung von Tumorwachstum verbessern.

Key words

CT - image manipulation/reconstruction - technical aspects

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