Volumetric growth assessment of pulmonary lesions is crucial to both lung cancer screening and oncological therapy monitoring. While several methods for small pulmonary nodules have previously been presented, the segmentation of larger tumors that appear frequently in oncological patients and are more likely to be complexly interconnected with lung morphology has not yet received much attention. We present a fast, automated segmentation method that is based on morphological processing and is suitable for both small and large lesions. In addition, the proposed approach addresses clinical challenges to volume assessment such as variations in imaging protocol or inspiration state by introducing a method of segmentation-based partial volume analysis (SPVA) that follows on the segmentation procedure. Accuracy and reproducibility studies were performed to evaluate the new algorithms. In vivo interobserver and interscan studies on low-dose data from eight clinical metastasis patients revealed that clinically significant volume change can be detected reliably and with negligible computation time by the presented methods. In addition, phantom studies were conducted. Based on the segmentation performed with the proposed method, the performance of the SPVA volumetry method was compared with the conventional technique on a phantom that was scanned with different dosages and reconstructed with varying parameters. Both systematic and absolute errors were shown to be reduced substantially by the SPVA method. The method was especially successful in accounting for slice thickness and reconstruction kernel variations, where the median error was more than halved in comparison to the conventional approach.

Owing to the rapid development of scanner technology, thoracic computed tomography (CT) offers new possibilities but also faces enormous challenges with respect to the quality of computer-assisted diagnosis and therapy planning. In the framework of the Virtual Institute for Computer Assistance in Clinical Radiology cooperative research project, a prototypical software application was developed to assist the radiologist in functional analysis of thoracic CT data. By identifying the anatomic compartments of the lungs, the software application enables assessment of established functional CT parameters for each individual lung, pulmonary lobe, and pulmonary segment. Such region-based assessment allows a more localized diagnosis of lung diseases such as emphysema and more accurate estimation of regional lung function from CT data. With close cooperation between computer scientists and radiologists, the software application was tested and optimized to achieve a high degree of usability. Several clinical studies were carried out, the results of which indicated that the software application improves quantification in diagnosis, therapy planning, and therapy monitoring with respect to accuracy and time required.

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Semiautomated segmentation of brain metastases on the basis of CE-MRI yielded reproducible volume measurements with a lower variability compared with linear measurements. Volumetry of contrast-enhancing brain metastases appears to be a suitable method for size determination in oncologic follow-up imaging.

Therapy monitoring in oncological patient care requires accurate and reliable imaging and post-processing methods. RECIST criteria are the current standard, with inherent disadvantages. The aim of this study was to investigate the feasibility of semi-automated volumetric analysis of lymph node metastases in patients with malignant melanoma compared to manual volumetric analysis and RECIST. Multislice CT was performed in 47 patients, covering the chest, abdomen and pelvis. In total, 227 suspicious, enlarged lymph nodes were evaluated retrospectively by two radiologists regarding diameters (RECIST), manually measured volume by placement of ROIs and semi-automated volumetric analysis. Volume (ml), quality of segmentation (++/--) and time effort (s) were evaluated in the study. The semi-automated volumetric analysis software tool was rated acceptable to excellent in 81% of all cases (reader 1) and 79% (reader 2). Median time for the entire segmentation process and necessary corrections was shorter with the semi-automated software than by manual segmentation. Bland-Altman plots showed a significantly lower interobserver variability for semi-automated volumetric than for RECIST measurements. The study demonstrated feasibility of volumetric analysis of lymph node metastases. The software allows a fast and robust segmentation in up to 80% of all cases. Ease of use and time needed are acceptable for application in the clinical routine. Variability and interuser bias were reduced to about one third of the values found for RECIST measurements.