Interactive segmentation framework of the Medical Imaging Interaction Toolkit

Maleike, Daniel; Nolden, Marco; Meinzer, Hans Peter; Wolf, Ivo

doi:10.1016/j.cmpb.2009.04.004

Cited by 89 publications

(53 citation statements)

References 8 publications

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“…The semiautomatic interactive segmentation exploits a geodesic active contour model, according to image gray‐scale and spatial information, to delineate the liver boundary 18 , 19 . The liver segmentation was also performed by the surgeon who conducted the manual liver volumetry.…”

Section: Methodsmentioning

confidence: 99%

Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches

Cai

Fan

et al. 2016

J Applied Clin Med Phys

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This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods— one interactive method, an in‐house‐developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)‐based segmentation, and one automatic probabilistic atlas (PA)‐guided segmentation method on clinical contrast‐enhanced CT images. Forty‐two datasets, including 27 normal liver and 15 space‐occupying liver lesion patients, were retrospectively included in this study. The three methods — one semiautomatic 3DMIA, one automatic ASM‐based, and one automatic PA‐based liver volumetry — achieved an accuracy with VD (volume difference) of −1.69%,−2.75%, and 3.06% in the normal group, respectively, and with VD of −3.20%,−3.35%, and 4.14% in the space‐occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excellent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (p<0.001), as well as between the automatic volumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (p<0.001). The semiautomatic interactive 3DMIA, automatic ASM‐based, and automatic PA‐based liver volumetry agreed well with manual gold standard in both the normal liver group and the space‐occupying lesion group. The ASM‐ and PA‐based automatic segmentation have better efficiency in clinical use.PACS number(s): 87.55.‐x

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Section: Methodsmentioning

confidence: 99%

Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches

Cai

Fan

et al. 2016

J Applied Clin Med Phys

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“…An experienced operator (DM) manually segmented each CT volume using the Medical Imaging Interaction Toolkit (MITK) version 0.12.2 [20]. Segmentation was performed using free-hand drawing on intermittent slices with intermediate contours completed by interpolation.…”

Section: Manual Segmentationmentioning

confidence: 99%

Voxel classification and graph cuts for automated segmentation of pathological periprosthetic hip anatomy

2012

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Purpose Automated patient-specific image-based segmentation of tissues surrounding aseptically loose hip prostheses is desired. For this we present an automated segmentation pipeline that labels periprosthetic tissues in computed tomography (CT). The intended application of this pipeline is in pre-operative planning. Methods Individual voxels were classified based on a set of automatically extracted image features. Minimum-cost graph cuts were computed on the classification results. The graphcut step enabled us to enforce geometrical containment constraints, such as cortical bone sheathing the femur's interior. The solution's novelty lies in the combination of voxel classification with multilabel graph cuts and in the way label costs were defined to enforce containment constraints.Results The segmentation pipeline was tested on a set of twelve manually segmented clinical CT volumes. The distribution of healthy tissue and bone cement was automatically determined with sensitivities greater than 82% and pathological fibrous interface tissue with a sensitivity exceeding 73%. Specificity exceeded 96% for all tissues. Conclusions The addition of a graph-cut step improved segmentation compared to voxel classification alone. The pipeline described in this paper represents a practical approach to segmenting multitissue regions from CT.

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“…), who were blinded for the treatment phase of the respective CT scan. Arterial phase images were included in our analysis in order to differentiate organ borders using an in-house-developed software (MITK Version 2011; DKFZ, Heidelberg, Germany) [8,9]. The organ was segmented by exploiting the local gray-value statistics of the pancreas tissue (fig.…”

Section: Methodsmentioning

confidence: 99%

Pancreatic Atrophy in Hepatocellular Carcinoma Patients Receiving Long-Term Treatment with Sorafenib

et al. 2015

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Objective: To date, sorafenib is the only approved systemic therapy for advanced hepatocellular carcinoma (HCC). Pancreatic atrophy has recently been reported in 2 patients as a novel side effect after long-term sorafenib treatment. Methods: We retrospectively analyzed clinical and radiological data of patients with advanced HCC with long-term treatment of sorafenib (median 279 days, range 153-826 days). Pancreata were semi-manually segmented section by section to calculate the pancreas volumes before and under sorafenib treatment. Results: Sorafenib reduced pancreatic volume in 18/19 (95%) HCC patients with a mean pancreatic volume loss of 25% (p = 0.002). Pancreatic volume loss depended on the dose (r = 0.36) and exposure time of sorafenib (r = 0.35) and was detectable as early as after 3 months of sorafenib treatment and already after a cumulative sorafenib dose of <100 g. Median overall survival was 13.2 months (range 7.8-31.3 months) but did not correlate with sorafenib-induced pancreatic volume reduction (hazard ratio 1.002, 95% confidence interval 0.981-1.060, p = 0.24). Conclusion: We could confirm pancreatic atrophy as a novel adverse event of sorafenib therapy in HCC patients, correlating with sorafenib dose and exposure time.

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Interactive segmentation framework of the Medical Imaging Interaction Toolkit

Cited by 89 publications

References 8 publications

Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches

Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches

Voxel classification and graph cuts for automated segmentation of pathological periprosthetic hip anatomy

Pancreatic Atrophy in Hepatocellular Carcinoma Patients Receiving Long-Term Treatment with Sorafenib

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