Pulmonary Nodule Segmentation In Lung Ct Images By Post Processing U-nets Using Average Ensemble Learning Technique

Pulmonary nodules are potential manifestation of lung cancer and accurate segmentation ofrndifferent pulmonary nodules from lung Computed Tomography (CT) scan images is importantrnclinical relevance in diagnosis, prognosis and treatment of lung cancer. However, due to thernhighly heterogeneous type, size, location and shape of nodules, segmentation of pulmonaryrnnodules is very challenging. In this study we propose and present an improved pulmonary nodulernsegmentation method based on thresholding and morphological operators, lungs regionrnsegmentation algorithm, modified U-Net model for pulmonary nodules detection andrnsegmentation, and average ensemble learning as a post processing technique. First, as a preprocessingrnstagernthatrnincludesrnlungsrnregionrnsegmentation,rnnormalization,rncroppingrnandrnresizingrnrnhasrndone onrnthernrawrninputrnCTrnscanrnimages.rnThresholdingrnandrnmorphologicalrnoperatorsrnalgorithmrnrnisrnsimplernandrnyieldsrngoodrnresultrninrnaccuratelyrnsegmentingrnthernlungrnparenchymarntornreducernthernrnsearchrnspace.rnThenrnmodifiedrnU-Netrnmodelrnisrnloadedrntorndetectrnandrnsegmentrnpulmonaryrnnodules.rnrnU-NetrnisrnarnwidelyrnusedrnConvolutionalrnNeuralrnNetworkrn(CNN)rnstructurernforrnend-to-endrnrnsegmentationrnandrncanrnbernusedrnonrnthernentirernimagernclassificationrnlabelsrnoverrnthernentirerninputrnpixelsrnrnsornthatrnitrnisrnmorernefficientrnandrnexpectedrntornyieldrnbetterrnperformance.rnFurthermore,rninsteadrnofrnrnpickingrnthernbestrnU-Netrnnetworkrnstructure,rnwernappliedrnaveragernensemblernlearningrnmethodrnasrnarnrnpostrnprocessingrntechnique.rnAnrnensemblernofrnthreernU-Netrnmodelsrnhavingrndifferentrnnetworkrnrnstructure,rntrainedrnonrnthernsamerndatasetrnwithrndifferentrnhyper-parameters,rncanrngenerallyrnimprovernthernrnoverallrnsegmentationrnperformance.rnThernperformancernofrnourrnproposedrnmethodrnisrntrained,rntested,rnrnandrnevaluatedrnusingrn858rnlungrnCTrnimagesrnandrntheirrncorrespondingrngroundrntruthrnnodulernmasksrnrnobtainedrnfromrnLungrnNodulernAnalysis 2016rn(LUNA16)rndatasetrnandrnachievedrnevaluationrnresultsrnofrnrn0.848rnmeanrnDicernSimilarityrnCoefficientrn(DSC),rn0.965rnmeanrnaccuracy,rn0.826rnmeanrnsensitivity,rnrnand 0.983rnmeanrnspecificity.rnMoreover,rnwerncomparedrnourrntestrnresultsrnwithrnotherrnmethodsrnresultsrnrntornshowrnourrnapproach’srnperformance.rnExperimentsrnandrnthesernpreliminaryrnresultsrnshowedrnthatrnourrnrnproposedrnmethodrncanrneffectivelyrnimprovernthernsegmentationrnaccuracyrnofrnpulmonaryrnnodulesrnandrnrntherneffectivenessrnofrnourrnapproach.rnGenerally,rnthernalgorithmsrnusedrnherernarernsimple,rneffectivernandrnrnpractical.

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