Advanced Segmentation Techniques for Lung Nodules, Liver Metastases, and Enlarged Lymph Nodes in CT Scans

Moltz, Jan Hendrik; Bornemann, L.; Kuhnigk, Jan-Martin; Dicken, Volker; Peitgen, E.; Meier, S; Bolte, Hendrik; Fabel, M.; Bauknecht, Hans-Christian; Hittinger, M.; Kießling, Andreas; Püsken, Michael; Peitgen, Heinz‐Otto

doi:10.1109/jstsp.2008.2011107

Cited by 136 publications

(75 citation statements)

References 19 publications

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“…From the results presented in Table 6.13 the algorithm estimated the volume of the tumor(s) from CT images with an accuracy of about 86% for the 13 datasets out of the 20 that had livers with tumors. Although the accuracy is not as high as the liver segmentation approach, this accuracy is comparable to the results obtained from different studies (Abdel-massieh et al, 2010;Choudhary et al, 2008;Moltz et al, 2008;Qi et al, 2008;Xu et al, 2010;Yoav et al, 2008).…”

Section: Tumor and Blood Vessel Segmentationsupporting

confidence: 86%

A Novel 3-D Segmentation Algorithm for Anatomic Liver and Tumor Volume Calculations for Liver Cancer Treatment Planning

Goryawala¹

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Section: Tumor and Blood Vessel Segmentationsupporting

confidence: 86%

A Novel 3-D Segmentation Algorithm for Anatomic Liver and Tumor Volume Calculations for Liver Cancer Treatment Planning

Goryawala¹

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“…Oncology prototype software (Version 1.9.0; 2012-11-15 Release; Qt Version 4.8.0 Fraunhofer MEVIS, Siemens) was used for semi-automated 3D segmentation of liver lesions (11). There is no general consent on a gold standard for volumetric lesion quantification in the presented scenario yet.…”

Section: Image Analysis and Statisticsmentioning

confidence: 99%

“…There is no general consent on a gold standard for volumetric lesion quantification in the presented scenario yet. However, the Fraunhofer method/algorithm has been tested and shown to be reliable (11). In each patient, the Gd-EOB-DTPA MRI dataset underwent volumetric analysis of all identifiable lesions in a semi-automated manner as shown in Figs.…”

Section: Image Analysis and Statisticsmentioning

confidence: 99%

Semi-automatic 3D-volumetry of liver metastases from neuroendocrine tumors to improve combination therapy with 177Lu-DOTATOC and 90Y-DOTATOC

Cieciera

Kratochwil

Moltz

et al. 2016

Diagn Interv Radiol

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PURPOSE Patients with neuroendocrine tumors (NET) often present with disseminated liver metastases and can be treated with a number of different nuclides or nuclide combinations in peptide receptor radionuclide therapy (PRRT) depending on tumor load and lesion diameter. For quantification of disseminated liver lesions, semi-automatic lesion detection is helpful to determine tumor burden and tumor diameter in a time efficient manner. Here, we aimed to evaluate semi-automated measurement of total metastatic burden for therapy stratification. METHODS Nineteen patients with liver metastasized NET underwent contrast-enhanced 1.5 T MRI using gadolinium-ethoxybenzyl diethylenetriaminepentaacetic acid. Liver metastases (n=1537) were segmented using Fraunhofer MEVIS Software for three-dimensional (3D) segmentation. All lesions were stratified according to longest 3D diameter >20 mm or <= 20 mm and relative contribution to tumor load was used for therapy stratification. RESULTS Mean count of lesions <= 20 mm was 67.5 and mean count of lesions > 20 mm was 13.4. However, mean contribution to total tumor volume of lesions <= 20 mm was 24%, while contribution of lesions > 20 mm was 76%. CONCLUSION Semi-automatic lesion analysis provides useful information about lesion distribution in predominantly liver metastasized NET patients prior to PRRT. As conventional manual lesion measurements are laborious, our study shows this new approach is more efficient and less operator-dependent and may prove to be useful in the decision making process selecting the best combination PRRT in each patient

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“…The software uses a dedicated algorithm for the segmentation of liver lesions based on lesion density, image noise and contrast difference with respect to the adjacent liver parenchyma. After an initial diameter is drawn by the reader in an arbitrary slice on a liver lesion, region-growing-based algorithms are used in combination with watershed transformation and distance transformation algorithms for the segmentation and separation of adjacent structures of similar density (e. g. vessels, bile ducts or adjacent adipose tissue) [16]. As the purpose of this study was to analyze the influence of different reconstruc- tion kernels on semi-automated segmentation, any reader-dependent influence had to be excluded.…”

Section: Semi-automated Segmentationmentioning

confidence: 99%

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

Santos

Kloeckner

Wunder

et al. 2014

Fortschr Röntgenstr

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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, Kl?ckner 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 R?ntgenstr 2014; 186: 780???784

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Advanced Segmentation Techniques for Lung Nodules, Liver Metastases, and Enlarged Lymph Nodes in CT Scans

Cited by 136 publications

References 19 publications

A Novel 3-D Segmentation Algorithm for Anatomic Liver and Tumor Volume Calculations for Liver Cancer Treatment Planning

A Novel 3-D Segmentation Algorithm for Anatomic Liver and Tumor Volume Calculations for Liver Cancer Treatment Planning

Semi-automatic 3D-volumetry of liver metastases from neuroendocrine tumors to improve combination therapy with 177Lu-DOTATOC and 90Y-DOTATOC

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

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