Int J Comput Assist Radiol Surg - Object-based analysis of CT images for automatic detection and segmentation of hypodense liver lesions.

Tópicos

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Resumo

RPOSE: Hypodense liver lesions are commonly detected in CT, so their segmentation and characterization are essential for diagnosis and treatment. Methods for automatic detection and segmentation of liver lesions were developed to support this task.METHODS: The detection algorithm uses an object-based image analysis approach, allowing for effectively integrating domain knowledge and reasoning processes into the detection logic. The method is intended to succeed in cases typically difficult for computer-aided detection systems, especially low contrast of hypodense lesions relative to healthy tissue. The detection stage is followed by a dedicated segmentation algorithm needed to synthesize 3D segmentations for all true-positive findings.RESULTS: The automated method provides an overall detection rate of 77.8% with a precision of 0.53 and performs better than other related methods. The final lesion segmentation delivers appropriate quality in 89% of the detected cases, as evaluated by two radiologists.CONCLUSIONS: A new automated liver lesion detection algorithm employs the strengths of an object-based image analysis approach. The combination of automated detection and segmentation provides promising results with potential to improve diagnostic liver lesion evaluation.

Resumo Limpo

rpose hypodens liver lesion common detect ct segment character essenti diagnosi treatment method automat detect segment liver lesion develop support taskmethod detect algorithm use objectbas imag analysi approach allow effect integr domain knowledg reason process detect logic method intend succeed case typic difficult computeraid detect system especi low contrast hypodens lesion relat healthi tissu detect stage follow dedic segment algorithm need synthes d segment trueposit findingsresult autom method provid overal detect rate precis perform better relat method final lesion segment deliv appropri qualiti detect case evalu two radiologistsconclus new autom liver lesion detect algorithm employ strength objectbas imag analysi approach combin autom detect segment provid promis result potenti improv diagnost liver lesion evalu

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