Comput. Biol. Med. - Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models.


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We propose a computer-aided detection (CAD) system which can detect small-sized (from 3mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass-Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules.

Resumo Limpo

propos computeraid detect cad system can detect smallsiz mm pulmonari nodul spiral ct scan pulmonari nodul small lesion lung roundshap parenchym nodul wormshap juxtapleur nodul kind lesion radiodens greater lung parenchyma thus appear white imag lung nodul might indic lung cancer earli stage detect arguabl improv patient surviv rate ct consid accur imag modal nodul detect howev larg amount data per examin make full analysi difficult lead omiss nodul radiologist develop advanc computer method automat detect intern juxtapleur nodul lowdos thinslic lung ct scan method consist initi select nodul candid list segment candid nodul classif featur comput segment nodul candidateth present cad system aim reduc number omiss decreas radiologist scan examin time system locat scheme intern juxtapleur nodul correct volum segment lung parenchyma system use region grow rg algorithm open process includ juxtapleur nodul segment extract suspect nodular lesion ct imag lung cad system constitut hard task order solv key problem use new stabl d massspr model msm combin spline curv reconstruct process model repres concurr characterist gray valu rang direct contour inform well shape knowledg lead much robust effici segment process distinguish real nodul among nodul candid addit classif step appli furthermor neural network appli reduc fals posit fps doublethreshold cut system perform test set scan made avail lung imag databas consortium lidc annot four expert radiologist detect rate system fpsct reduct fpsct achiev sensit present new d segment techniqu lung nodul ct dataset use deform msms result effici segment process abl converg identifi shape generic roi iter suitabl result show use d ac model featur analysi base fps reduct process constitut accur approach segment classif lung nodul

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