Table Of ContentYufei Chen · Marius George Linguraru ·
Raj Shekhar · Stefan Wesarg ·
Marius Erdt · Klaus Drechsler ·
Cristina Oyarzun Laura (Eds.)
Clinical
6
4 Image-Based
7
3
1
S
Procedures
C
N
L
11th Workshop, CLIP 2022
Held in Conjunction with MICCAI 2022
Singapore, September 18, 2022, Proceedings
Lecture Notes in Computer Science 13746
FoundingEditors
GerhardGoos
KarlsruheInstituteofTechnology,Karlsruhe,Germany
JurisHartmanis
CornellUniversity,Ithaca,NY,USA
EditorialBoardMembers
ElisaBertino
PurdueUniversity,WestLafayette,IN,USA
WenGao
PekingUniversity,Beijing,China
BernhardSteffen
TUDortmundUniversity,Dortmund,Germany
MotiYung
ColumbiaUniversity,NewYork,NY,USA
Moreinformationaboutthisseriesathttps://link.springer.com/bookseries/558
· ·
Yufei Chen Marius George Linguraru
· · ·
Raj Shekhar Stefan Wesarg Marius Erdt
·
Klaus Drechsler Cristina Oyarzun Laura (Eds.)
Clinical
Image-Based
Procedures
11th Workshop, CLIP 2022
Held in Conjunction with MICCAI 2022
Singapore, September 18, 2022
Proceedings
Editors
YufeiChen MariusGeorgeLinguraru
TongjiUniversity Children’sNationalHealthSystem
Shanghai,China Washington,DC,USA
RajShekhar StefanWesarg
Children’sNationalHealthSystem FraunhoferIGD
Washington,DC,USA Darmstadt,Germany
MariusErdt KlausDrechsler
FraunhoferSingapore AachenUniversityofAppliedSciences
Singapore,Singapore Aachen,Germany
CristinaOyarzunLaura
FraunhoferIGD
Darmstadt,Germany
ISSN 0302-9743 ISSN 1611-3349 (electronic)
LectureNotesinComputerScience
ISBN 978-3-031-23178-0 ISBN 978-3-031-23179-7 (eBook)
https://doi.org/10.1007/978-3-031-23179-7
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Preface
The11thInternationalWorkshoponClinicalImage-basedProcedures:TowardsHolistic
PatientModelsforPersonalisedHealthcare(CLIP)washeldonSeptember18,2022,in
conjunctionwiththe25thInternationalConferenceonMedicalImageComputingand
ComputerAssistedIntervention(MICCAI2022).
FollowingthelongtraditionofCLIPontranslationalresearch,thegoaloftheworks
presented in this workshop is to bring basic research methods closer to the clinical
practice.Oneofthekeyaspectsthatisgainingrelevanceregardingtheapplicabilityof
basicresearchmethodsinclinicalpracticeisthecreationofHolisticPatientModelsasan
importantsteptowardspersonalisedhealthcare.Asamatteroffact,theclinicalpicture
ofapatientdoesnotuniquelyconsistofmedicalimages,butacombinationofmedical
imagedataofmultiplemodalitieswithotherpatientdata,e.g.,omics,demographicsor
electronic health records is desirable. Since 2019 CLIP has put a special emphasis on
thisareaofresearch.
CLIP 2022 received 12 submissions and 9 of them were accepted for publication.
Allsubmittedpaperswerepeer-reviewedbyatleast3experts.Allacceptedpaperswere
presentedbytheirauthorsduringtheworkshopandtheattendeeschosewiththeirvotes
theholderoftheBestPaperAwardofCLIP2022.Inadditiontotheoralpresentations
provided by the authors of the accepted papers, all attendees of CLIP 2022 had the
opportunity to enjoy high-quality keynotes followed by avid discussions in which all
attendeeswereinvolved.Wewouldliketothankourinvitedspeakersfortheirinteresting
talksanddiscussions:
Prof.XiahaiZhuang,FudanUniversity,Shanghai,China,“UsingStatisticalLearn-
ing to Improve Interpretation and Generalization in Medical Image Computing and
Analysis”(online).
Dr. Moti Freiman, Technion, Israel, “MR Physics Driven Artificial Intelligence”
(on-site).
Furthermore, we would like to take this opportunity to thank also our program
committeemembers,authorsandattendeeswhohelpedCLIP2022tobeagreatsuccess.
September2022 YufeiChen
MariusGeorgeLinguraru
RajShekhar
StefanWesarg
MariusErdt
KlausDrechsler
CristinaOyarzunLaura
Organization
OrganizingCommittee
YufeiChen TongjiUniversity,Shanghai,China
KlausDrechsler AachenUniversityofAppliedSciences,Germany
MariusErdt FraunhoferSingapore,Singapore
MariusGeorgeLinguraru Children’sNationalHealthcareSystem,USA
CristinaOyarzunLaura FraunhoferIGD,Germany
RajShekhar Children’sNationalHealthcareSystem,USA
StefanWesarg FraunhoferIGD,Germany
ProgramCommittee
NiklasBabendererde TechnicalUniversityDarmstadt,Germany
JanEgger GrazUniversityofTechnology,Austria
MotiFreiman Technion-IsraelInstituteofTechnology,Israel
MoritzFuchs TechnicalUniversityDarmstadt,Germany
CamilaGonzalez TechnicalUniversityDarmstadt,Germany
Anna-SophiaHertlein FraunhoferIGD,Germany
KatarzynaHeryan UniversityofScienceandTechnology,Poland
MartinHoßbach ClearGuideMedical,USA
YogeshKarpate ChistatsLabsPrivateLimited,India
PurnimaRajan ClearGuideMedical,USA
AndreasWirtz FraunhoferIGD,Germany
LukasZerweck FraunhoferITMP,Germany
StephanZidowitz FraunhoferMEVIS,Germany
Contents
FastAuto-differentiableDigitallyReconstructedRadiographsforSolving
InverseProblemsinIntraoperativeImaging ................................ 1
VivekGopalakrishnanandPolinaGolland
Multi-channelResidualNeuralNetworkBasedonSqueeze-and-Excitation
forOsteoporosisDiagnosis .............................................. 12
ChunmeiXia, YueDing, JionglinWu, WenqiangLuo, PeidongGuo,
TianfuWang,andBaiyingLei
MachineLearningBasedApproachforMotionDetectionandEstimation
inRoutinelyAcquiredLowResolutionNearInfraredFluorescenceOptical
Imaging ............................................................... 22
LukasZerweck,StefanWesarg,JörnKohlhammer,andMichaelaKöhm
AutomaticLandmarkIdentificationonIntraOralScans ....................... 32
BaptisteBaquero, MaximeGillot, LuciaCevidanes,
NajlaAlTurkestani,MarcelaGurgel,MathieuLeclercq,JonasBianchi,
MariliaYatabe, AntonioRuellas, CamilaMassaro, AronAliaga,
MariaAntoniaAlvarezCastrillon,DiegoRey,JuanFernandoAristizabal,
andJuanCarlosPrieto
STAU-Net:ASpatialStructureAttentionNetworkfor3DCoronaryArtery
Segmentation .......................................................... 43
GuanjieTong, HaijunLei, LiminHuang, ZhihuiTian, HaiXie,
BaiyingLei,andLongjiangZhang
Convolutional Redistribution Network for Multi-view Medical Image
Diagnosis ............................................................. 54
YuanZhou,XiaodongYue,YufeiChen,ChaoMa,andKeJiang
FeaturePatchBasedAttentionModelforDentalCariesClassification .......... 62
GenqiangRen,YufeiChen,ShuaiQi,YujieFu,andQiZhang
ConditionalDomainAdaptationBasedonInitialDistributionDiscrepancy
forEEGEmotionRecognition ............................................ 72
MohanZhao,LuPang,YanLu,FeiXie,ZhenghaoHe,XiaoliangGong,
andAnthonyGeorgeCohn
viii Contents
Automated Cone and Vessel Analysis in Adaptive Optics Like Retinal
ImagesforClinicalDiagnosticsSupport ................................... 82
Anna-SophiaHertlein,StefanWesarg,JessicaSchmidt,BenjaminBoche,
NorbertPfeiffer,andJulianeMatlach
AuthorIndex .......................................................... 91
Fast Auto-differentiable Digitally
Reconstructed Radiographs for Solving
Inverse Problems in Intraoperative
Imaging
B
Vivek Gopalakrishnan1,2( ) and Polina Golland1,2
1 Harvard-MIT Health Sciences and Technology,
Massachusetts Institute of Technology, Cambridge, MA, USA
2 Computer Science and Artificial Intelligence Laboratory,
Massachusetts Institute of Technology, Cambridge, MA, USA
{vivekg,polina}@csail.mit.edu
Abstract. The use of digitally reconstructed radiographs (DRRs) to
solveinverseproblemssuchasslice-to-volumeregistrationand3Drecon-
structioniswell-studiedinpreoperativesettings.Inintraoperativeimag-
ing,theutilityofDRRsislimitedbythechallengesingeneratingthemin
real-time and supporting optimization procedures that rely on repeated
DRR synthesis. While immense progress has been made in accelerat-
ing the generation of DRRs through algorithmic refinements and GPU
implementations, DRR-based optimization remains slow because most
DRR generators do not offer a straightforward way to obtain gradients
with respect to the imaging parameters. To make DRRs interoperable
withgradient-basedoptimizationanddeeplearningframeworks,wehave
reformulated Siddon’s method, the most popular ray-tracing algorithm
used in DRR generation, as a series of vectorized tensor operations. We
implementedthisvectorizedversionofSiddon’smethodinPyTorch,tak-
ing advantage of the library’s strong automatic differentiation engine to
make this DRR generator fully differentiable with respect to its param-
eters. Additionally, using GPU-accelerated tensor computation enables
ourvectorizedimplementationtoachieverenderingspeedsequivalentto
state-of-the-art DRR generators implemented in CUDA and C++. We
illustratetheresultingmethodinthecontextofslice-to-volumeregistra-
tion. Moreover, our simulations suggest that the loss landscapes for the
slice-to-volume registration problem are convex in the neighborhood of
the optimal solution, and gradient-based registration promises a much
fastersolutionthanprevailinggradient-freeoptimizationstrategies.The
proposedDRRgeneratorenablesfastcomputervisionalgorithmstosup-
portimageguidanceinminimallyinvasiveprocedures.Ourimplementa-
tion is publically available at https://github.com/v715/DiffDRR.
· ·
Keywords: DRRs Differentiable programming Inverse problems
(cid:2)c TheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerlandAG2023
Y.Chenetal.(Eds.):CLIP2022,LNCS13746,pp.1–11,2023.
https://doi.org/10.1007/978-3-031-23179-7_1
