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An Uncertainty-Driven Hybrid of Intensity-Based and Feature-Based Registration with Applica

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AnUncertainty-DrivenHybridof

Intensity-BasedandFeature-BasedRegistration

withApplicationtoRetinalandLungCT

Images󰀂

CharlesV.Stewart,Ying-LinLee,andChia-LingTsai

RensselaerPolytechnicInstituteTroy,NY12180-3590USA

Abstract.Anewhybridoffeature-basedandintensity-basedregistra-

tionispresented.Thealgorithmreflectsanewunderstandingoftheroleofalignmenterrorinthegenerationofregistrationconstraints.Thisleadstoaniterativeprocesswheredistinctiveimagelocationsfromthemovingimagearematchedagainsttheintensitystructureofthefixedimage.Thesearchrangeofthismatchingprocessiscontrolledbyboththeuncertaintyinthecurrenttransformationestimateandtheproper-tiesoftheimagelocationstobematched.TheresultinghybridalgorithmisappliedtoretinalimageregistrationbyincorporatingitasthemainestimationenginewithinourrecentlypublishedDual-BootstrapICPal-gorithm.Thehybridalgorithmisusedtoalignserialand4dCTimagesofthelungusingaB-splinebaseddeformationmodel.

1Introduction

Feature-basedandintensity-basedregistrationalgorithmsdifferconsiderablyin

theimage-basedfeaturesthatdrivethealignmentprocess[4].Intensity-basedtechniquesuseallimagepixelsanddonotrequireexplicitfeatureextraction.

Theytendtobemorestablearoundminimaoftheobjectivefunctionbecause

theydon’trelyonuncertainfeaturelocationsoroncorrespondenceswhichmay

fluctuatewithslightchangesinthetransformation.Ontheotherhand,feature-

basedtechniquestendtobefaster,haveawidercapturerange,andallowalign-

menttobefocusedononlyselectedsubsetsoftheimagedata.Thesestrengths

andweaknessesarestudiedexperimentallyin[9].

Agrowingsetofpapershasbeguntoaddresstheissueofcombiningfeature-

basedandintensity-basedregistration.Asexamples,in[5]registrationisdriven

bothbyintensitysimilarityerrorandbyerrorsinthepositionsofmatched

features.InFeldmaretal.[7]intensityistreatedasa4thdimensionforICP

matching,whileSharpetal.[13]combinesICPwithinvariantfeatures.Inamuch

󰀁ThesupportoftheGEGlobalResearchCenterandoftheNationalScienceFoun-dationthroughtheCenterforSubsurfaceSensingandImagingSystemsisgratefullyacknowledged.

C.Barillot,D.R.Haynor,andP.Hellier(Eds.):MICCAI2004,LNCS3216,pp.870–877,2004.c󰀂Springer-VerlagBerlinHeidelberg2004AnUncertainty-DrivenHybrid871

differentapproach,Aylwardetal.[1]locatetubularstructuresinoneimageand

thenalignimagesusinggradient-descentofa“tubularness”measureevaluated

atthetransformedlocationsofthesestructuresintheotherimage.Ourselin,et

al.[10]useblock-matchingofintensitiestogetherwithrobustregression.Shum

andSzeliski[15]useblockmatchingofintensitiestorefinevideomosaics.The

PASHAalgorithm[4]combinescorrespondenceanddeformationprocessesina

globalobjectivefunction.IntheHAMMERalgorithm[14]registrationisdriven

bythealignmentoffeaturepositionsbasedonahierarchicaldescriptionofthe

surroundingintensitydistribution.

Thispaperpresentsanewhybridofintensity-basedandfeature-basedregis-

trationandappliestheresultingalgorithmintwocontexts:aligninglow-overlap

retinaimagesandspline-basedalignmentoflungCTvolumes.Featurepoints

foundinthemovingimagearematchedagainsttheintensitystructureofthe

fixedimage.Importantly,thesearchrangeforthematchisdictatedbyboth

thepropertiesofthefeatureandtheuncertaintyinthecurrenttransforma-

tionestimate.Thisdiffersfromcommonintensity-basedtechniqueswherecon-

straintsaredrivenbylocalchangesinthesimilaritymeasure.Italsodiffersfrom

correspondence-basedmethodslikeICP[2]wherematchingispurelyanearest-

pointsearch.

Thiscorealgorithmisbuiltintotwodifferentoverallregistrationalgorithms.

ThefirstisanextensionofourrecentDual-BootstrapICP[17]algorithmfor

aligningretinalimages.Replacingthefeature-to-featureICPalgorithmwith

thenewfeature-to-intensitysimilaritymatchingincreasestheeffectivenessfor

extremelydifferentcases.Thesecondoverallalgorithmisanewtechniquefor

non-rigid,B-splinealignmentoflungCTvolumes.Smallregionswithsufficient

intensityvariationinthemovingimagearematchedagainstthefixedimage.

ThesematchescontroltheestimateofhierarchicalB-splinedeformations.The

algorithmisappliedtoeffectivelyalignserialCTvolumes.

2TheRoleofUncertaintyinRegistration

Wemotivatethenewhybridalgorithmbyconsideringtheimportanceofun-

certaintyinregistration.LetS(p,q)measuretheregion-basedsimilarityerror

betweenmovingimageImatlocationpandfixed(target)imageIfatlocation

q.LetT(p;θ)bethetransformationmappingpfromImontoIfbasedonthe

(tobeestimated)parametervectorθ.Finally,letPbeasetoflocationsinImwheretransformationconstraintsareapplied.

Intensity-basedalgorithmssearchforthetransformationminimizingtheag-

gregatesimilarityerror:

E(θ)=󰀂

pi∈PS(pi,T(pi,θ)),(1)

Regularizingconstraintsmaybeplacedonθ,butwewillignorethesefornow.E(θ)ismostfrequentlyminimizedthroughagradient-descenttechnique[8].

Theestimate,ˆθ,remainsuncertainthroughouttheminimizationprocess.This

合集下载

多期相CT合成辅助的腹部多器官图像分割

多期相CT合成辅助的腹部多器官图像分割

在定制放射治疗计划（RTP）过程中，需要对病人进

行增强CT扫描与普通CT扫描［1］。其中，增强CT扫描

通过对患者进行静脉注射造影剂后，根据造影剂提高病

变组织与邻近正常组织之间的密度差异率［2,3］的特点，在

不同的时间段中造影剂代谢的效率进行CECT扫描，得

到动脉期、静脉期与延迟期图像，结合多期相CT影像辅

助医生达到可靠的诊断目的［4］；而普通CT扫描得到的

平扫CT图像在放射治疗计划定制过程中用于计算放射治疗的剂量［5］。在分别进行增强CT扫描与普通CT扫

描后，医生联合平扫CT与增强CT实现对靶区与危及器

官的勾画。为了节省医生对危及器官勾画的工作量与

时间，基于深度学习的自动建模方式能够极大地提升勾

画效率，然而，现有的腹部器官分割方法［6-14］多采用单期

相图像［15］进行分割。不同期相的CT图像能够为同一分

割目标提供不同的成像细节与纹理结构［16］，在放射治疗

定制的过程中同样需要联合多期相CT图像进行危及器

官与靶区的勾画。然而，不同期相CT的图像信息在当

前大部分自动分割方法中并未被充分利用。

进行多期相CT扫描，往往耗费大量的时间与程序，

尤其在进行延迟期CECT的扫描时，需要病人保持静止

姿势长达至少3~5min，患者可能存在无法忍受的痛苦、

静止性震颤等原因无法坚持完整的扫描，在辐射场合中

长时间的暴露也存在健康的风险。同时，扫描获得的多多期相CT合成辅助的腹部多器官图像分割

黄品瑜1，钟丽明1，郑楷宜1，陈泽立1，肖若琳1，全显跃2，阳维1

南方医科大学1生物医学工程学院//广东省医学图像处理重点实验室，广东广州510515；2南方医科大学珠江

医院影像诊断科，广东广州510282

Multi-phaseCTsynthesis-assistedsegmentationofabdominalorgans

HUANGPinyu1,ZHONGLiming1,ZHENGKaiyi1,CHENZeli1

,XIAORuolin1,QUANXianyue2,YANGWei11SchoolofBiomedicalEngineering,SouthernMedicalUniversity//GuangdongProvincialKeyLaboratoryofMedicalImageProcessing,Guangzhou510515,China;2DepartmentofRadiology,ZhujiangHospital,SouthernMedicalUniversity,Guangzhou510282,China摘要：目的提出并探讨使用多期相CT合成辅助腹部多器官分割方法。方法提出多期相CT合成辅助腹部多器官分割，多期相CT能够充分提供同一器官不同的图像细节，从而为分割模型提供充分的全面的语义信息，提升腹部多个器官分割的性能。提

Intensity gradient based registration and fusion of multi-modal images

Intensity gradient based registration and fusion of multi-modal images

Intensitygradientbasedregistrationandfusion

ofmulti-modalimages

EldadHaber1andJanModersitzki2

1MathematicsandComputerScience,EmoryUniversity,Atlanta,GA,USA,haber@mathcs.emory,edu2InstituteofMathematics,L¨ubeck,Germany,modersitzki@math.uni-luebeck.de

Abstract.Aparticularprobleminimageregistrationarisesformulti-modalimagestakenfromdifferentimagingdevicesand/ormodalities.Startingin1995,mutualinformationhasshowntobeaverysuccessfuldistancemeasureformulti-modalimageregistration.However,mutualinformationhasalsoanumberofwell-knowndrawbacks.Itsmaindis-advantageisthatitisknowntobehighlynon-convexandhastypicallymanylocalmaxima.Thisobservationmotivateustoseekadifferentimagesimilaritymea-surewhichisbettersuitedforoptimizationbutaswellcapabletohandlemulti-modalimages.Inthisworkweinvestigateanalternativedistancemeasurewhichisbasedonnormalizedgradientsandcompareitsperfor-mancetoMutualInformation.WecallthenewdistancemeasureNor-malizedGradientFields(NGF).

ICDAR2011

ICDAR2011

TextDetectionandCharacterRecognitioninSceneImages

withUnsupervisedFeatureLearning

AdamCoates,BlakeCarpenter,CarlCase,SanjeevSatheesh,BipinSuresh,TaoWang,DavidJ.Wu,AndrewY.Ng

ComputerScienceDepartment

StanfordUniversity

353SerraMall

Stanford,CA94305USA

{acoates,blakec,cbcase,ssanjeev,bipins,twangcat,dwu4,ang}@

Abstract—Readingtextfromphotographsisachallengingproblemthathasreceivedasignificantamountofattention.Twokeycomponentsofmostsystemsare(i)textdetectionfromimagesand(ii)characterrecognition,andmanyrecentmethodshavebeenproposedtodesignbetterfeaturerepresentationsandmodelsforboth.Inthispaper,weapplymethodsrecentlydevelopedinmachinelearning–specifically,large-scalealgo-rithmsforlearningthefeaturesautomaticallyfromunlabeleddata–andshowthattheyallowustoconstructhighlyeffectiveclassifiersforbothdetectionandrecognitiontobeusedinahighaccuracyend-to-endsystem.

segment anything model解读

segment anything model解读

segment anything model解读

Segmentation is a vital task in computer vision that involves identifying

and delineating objects or regions of interest within an image. One highly

effective approach to segmentation is the use of the "segment anything"

model. This model has garnered significant attention in the field due to its

ability to accurately segment a wide range of objects, regardless of their size,

shape, or context.

The segment anything model is based on a deep learning architecture

called a convolutional neural network (CNN). CNNs possess the remarkable

ability to learn meaningful representations directly from raw pixel data by

repeatedly applying convolutional and pooling operations. This enables the

model to capture both low-level and high-level features that are essential for

accurate segmentation.