Int J Comput Assist Radiol Surg - Rapid image recognition of body parts scanned in computed tomography datasets.

Tópicos

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Resumo

M: Automatic CT dataset classification is important to efficiently create reliable database annotations, especially when large collections of scans must be analyzed.METHOD: An automated segmentation and labeling algorithm was developed based on a fast patient segmentation and extraction of statistical density class features from the CT data. The method also delivers classifications of image noise level and patient size. The approach is based on image information only and uses an approximate patient contour detection and statistical features of the density distribution. These are obtained from a slice-wise analysis of the areas filled by various materials related to certain density classes and the spatial spread of each class. The resulting families of curves are subsequently classified using rules derived from knowledge about features of the human anatomy.RESULTS: The method was successfully applied to more than 5,000 CT datasets. Evaluation was performed via expert visual inspection of screenshots showing classification results and detected characteristic positions along the main body axis. Accuracy per body region was very satisfactory in the trunk (lung/liver >99.5% detection rate, presence of abdomen >97% or pelvis >95.8%) improvements are required for zoomed scans.CONCLUSION: The method performed very reliably. A test on 1,860 CT datasets collected from an oncological trial showed that the method is feasible, efficient, and is promising as an automated tool for image post-processing.

Resumo Limpo

m automat ct dataset classif import effici creat reliabl databas annot especi larg collect scan must analyzedmethod autom segment label algorithm develop base fast patient segment extract statist densiti class featur ct data method also deliv classif imag nois level patient size approach base imag inform use approxim patient contour detect statist featur densiti distribut obtain slicewis analysi area fill various materi relat certain densiti class spatial spread class result famili curv subsequ classifi use rule deriv knowledg featur human anatomyresult method success appli ct dataset evalu perform via expert visual inspect screenshot show classif result detect characterist posit along main bodi axi accuraci per bodi region satisfactori trunk lungliv detect rate presenc abdomen pelvi improv requir zoom scansconclus method perform reliabl test ct dataset collect oncolog trial show method feasibl effici promis autom tool imag postprocess

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