Name
Title | ||
|---|---|---|
Segmentation-Based Partial Volume Correction for Volume Estimation of Solid Lesions in CT |
Abstract | ||
|---|---|---|
In oncological chemotherapy monitoring, the change of a tumor's size is an important criterion for assessing cancer therapeutics. Measuring the volume of a tumor requires its delineation in 3-D. This is called segmentation, which is an intensively studied problem in medical image processing. However, simply counting the voxels within a binary segmentation result can lead to significant differences in the volume, if the lesion has been segmented slightly differently by various segmentation procedures or in different scans, for example due to the limited spatial resolution of computed tomography (CT) or partial volume effects. This variability limits the sensitivity of size measurements and thus of therapy response assessments and it can even lead to misclassifications. We present a fast, generic algorithm for measuring the volume of solid, compact tumors in CT that considers partial volume effects at the border of a given segmentation result. The algorithm is an extension of the segmentation-based partial volume analysis proposed by Kuhnigk for the volumetry of solid lung lesions , such that it can be applied to inhomogeneous lesions and lesions with inhomogeneous surroundings. Our generalized segmentation-based partial volume correction is based on a spatial subdivision of the segmentation result, from which the fraction of tumor for each voxel is computed. It has been evaluated on phantom data, 1516 lesion segmentation pairs (lung nodules, liver metastases and lymph nodes) as well as 1851 lung nodules from the LIDC-IDRI database. The evaluations of our algorithm show a more accurate estimation of the real volume and its ability to reduce inter- and intra-observer variability significantly for each entity. Overall, the variability (interquartile range) for phantom data is reduced by 49% ( p ≪ 0.001) and the variability between different readers is reduced by 28% ( p ≪ 0.001). The average computation time is 0.2 s. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TMI.2013.2287374 | Medical Imaging, IEEE Transactions |
Keywords | Field | DocType |
cancer,computerised tomography,image resolution,image segmentation,liver,lung,medical image processing,phantoms,sensitivity,size measurement,tumours,volume measurement,3D delineation,LIDC-IDRI database,binary segmentation,cancer therapeutics,compact tumors,computed tomography,generic algorithm,inhomogeneous lesions,inhomogeneous surroundings,interobserver variability,interquartile range,intraobserver variability,lesion segmentation pairs,limited spatial resolution,liver metastases,lung nodules,lymph nodes,medical image processing,oncological chemotherapy monitoring,phantom data,segmentation-based partial volume correction,sensitivity,size measurements,solid lesions,solid lung lesions,spatial subdivision,therapy response assessments,tumor size,volume estimation,volume measurement,Computed tomography (CT),partial volume correction,quantification,response evaluation criteria in solid tumors (RECIST),tumors,volumetry | Nuclear medicine,Voxel,Imaging phantom,Image processing,Image segmentation,Artificial intelligence,Partial volume correction,Computer vision,Segmentation,Radiology,Image resolution,Partial volume,Mathematics | Journal |
Volume | Issue | ISSN |
33 | 2 | 0278-0062 |
Citations | PageRank | References |
2 | 0.42 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Heckel | 1 | 41 | 4.59 |
| Hans Meine | 2 | 38 | 6.05 |
| Jan Hendrik Moltz | 3 | 80 | 8.85 |
| Jan-martin Kuhnigk | 4 | 265 | 21.86 |
| Johannes T Heverhagen | 5 | 2 | 1.43 |
| Andreas Kiessling | 6 | 68 | 3.69 |
| Boris Buerke | 7 | 2 | 0.42 |
| Horst K. Hahn | 8 | 450 | 72.61 |