Atlas&BasedUnderSegmentaon* - People · 2014. 9. 21. · zix ⇠ Cat(⇡) ⇡ ⇠ GEM(↵) (µk,...

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WM GM HC CA PU PA AM AC VE 0 20 40 60 80 *** *** *** ** *** *** *** *** *** Background Overand undersegmentation (%) AtlasBased UnderSegmenta1on Chris&an Wachinger 1,2 , Polina Golland 1 1 Computer Science and Ar&ficial Intelligence Lab (CSAIL), MIT 2 MassachuseDs General Hospital, Harvard Medical School Acknowledgement: We thank Jason Chang and Greg Sharp. This work was supported in part by the Na&onal Alliance for Medical Image Compu&ng (U54EB005149) and the NeuroImaging Analysis Center (P41EB015902). Introduc1on Atlasbased segmenta&on is commonly used in medical image analysis Tendency of atlasbased segmenta&on to undersegment structures Dice overlap and Hausdorff distance do not measure undersegmenta&on Wang, Yushkevich (2012) proposed deconvolu&on of label maps Contribu&ons: Volume overlap measures to quan&fy undersegmenta&on Hypothesis: asymmetry in single organ segmenta1on as cause Genera&ve model of background to correct undersegmenta&on Conclusions Significant bias in atlasbased segmenta&on to undersegmenta&on Asymmetry in foregroundbackground segmenta&on as new hypothesis Genera&ve model to par&&on background reduces undersegmenta&on UnderSegmenta1on Measures to quan&fy over and undersegmenta&on: Results Datasets: 16 heart MRA images (0.6x0.6x1.5mm) 18 head and neck CT scans (0.9x0.9x2.5mm) 39 brain MRI with 1mm isotropic resolu&on Comparison to deconvolu&on (Wang, 2012) Details: DPGMM, GMM, DPmeans, kmeans Global and local approach Patch size: 3 x 3 x 3 WM GM HC CA PU PA AM AC VE 0 20 40 60 80 *** ** Overand undersegmentation (%) O ( ˆ S, ¯ S )= | ˆ S \ ¯ S | | ¯ S | U ( ˆ S, ¯ S )= | ¯ S \ ˆ S | | ¯ S | λ 1 N P ix z ix μ k , k WM GM HC CA PU PA AM AC VE 0 20 40 60 80 *** *** ** *** *** Comparison of overand undersegmentation, Latent Labels Overand undersegmentation (%) WM GM HC CA PU PA AM AC VE 0.02 0 0.02 0.04 ** ** ** *** ** * ** ** * Dice difference Improvement over foregrbackgr for multiorgan and latent label IW Deconv gkmeans gDPmeans gGMM gDPGMM lkmeans lDPmeans 0.75 0.8 0.85 0.9 Dice Left Parotid Gland IW Deconv gkmeans gDPmeans gGMM gDPGMM lkmeans lDPmeans 0.6 0.65 0.7 0.75 0.8 0.85 Dice Right Parotid Gland IW Deconv gkmeans gDPmeans gGMM gDPGMM lkmeans lDPmeans 0.88 0.9 0.92 0.94 0.96 Dice Quan&ta&ve segmenta&on results for three applica&ons Undersegmenta&on errors significantly higher than oversegmenta&on errors White maDer (WM), Gray maDer (GM), Hippocampus (HC), Caudate (CA), Putamen (PU), Pallidum (PA), Amygdala (AM), Accumbens (AC), Ventricles (VE) First column: oversegmenta&on Second column: undersegmenta&on Hypothesis for undersegmenta1on Undersegmenta&on is caused by asymmetry in foregroundbackground segmenta&on Merging all surrounding labels into background creates a new metalabel that dominates the vo&ng process Mul&organ segmenta&on of brain supports hypothesis Latent mul1label model of the background Genera&ve model for the unsupervised separa&on of the background in K components and simultaneous es&ma&on of K Dirichlet process Gaussian mixture model (DPGMM) on patches: Replace background label with mixture component: z ix 2 {1,...,K } S i (x)= z ix P = {P ix : x 2 Γ,S i (x)= b} S i (x)= b Red: manual, Yellow: automa&c, White: voxel of interest Mul1atlas segmenta1on Test Image Registra&on Training Data Images Segmenta&ons Propaga&on Test Segmenta&on ? I = {I 1 ,...,I n } S = {S 1 ,...,S n } l 2 {1,..., } L l (x)= n X i=1 p(S (x)= l |S i ) · p(I (x)|I i ) p(S (x)= l |S i )= 1 if S i (φ i (x)) = l, 0 otherwise. p(I (x)|I i ) / exp ( -(I (x) - I i (φ i (x))) 2 /2σ 2 ) ˆ S (x) = arg max l L l (x) L f (x) >L b (x) S Label maps specify likelihood of each label: Label likelihood term: Intensity likelihood term: Most likely label yields segmenta&on: ForegroundBackground segmenta&on: Red: manual, Yellow: automa&c Segmenta&on of paro&d glands in CT images Organ Background Merging several structures to one background label Left Parotid Right Parotid 0 10 20 30 40 50 * ** Overand undersegmentation (%) Left Atrium 0 10 20 30 40 *** Overand undersegmentation (%) Paro&d glands in head & neck CT Lef atrium in heart MRA Nine brain structures in MRI Foregroundbackground segmenta&on Mul&organ brain segmenta&on Illustra&on of mul&organ and foregroundbackground segmenta&on 0 1 2 3 4 5 0.82 0.83 0.84 0.85 Dice Merged Structures Segmentation of Amygdala Backgrd AM 0 5 10 15 20 25 Votes FrgrdBckgrd Voting Lef Atrium Left Atrium 0 5 10 15 20 25 30 Overand undersegmentation (%) Left Parotid Right Parotid 0 10 20 30 40 50 Overand undersegmentation (%) Brain Paro&d Glands U O P ix |z ix N (μ z ix z ix ) z ix Cat() GEM() (μ k , k ) NW (λ) ¯ S : Manual ˆ S : Automatic segmentation ˆ S ¯ S WM GM HC AM Other 0 5 10 15 20 25 Votes MultiOrgan Voting

Transcript of Atlas&BasedUnderSegmentaon* - People · 2014. 9. 21. · zix ⇠ Cat(⇡) ⇡ ⇠ GEM(↵) (µk,...

Page 1: Atlas&BasedUnderSegmentaon* - People · 2014. 9. 21. · zix ⇠ Cat(⇡) ⇡ ⇠ GEM(↵) (µk, ⌃k) ⇠NW() S¯: Manual Sˆ: Automatic segmentation Sˆ S¯ WM GM HC AM Other 0

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Atlas-­‐Based  Under-­‐Segmenta1on  Chris&an  Wachinger1,2,  Polina  Golland1  

1Computer  Science  and  Ar&ficial  Intelligence  Lab  (CSAIL),  MIT              2MassachuseDs  General  Hospital,  Harvard  Medical  School  

Acknowledgement:  We  thank  Jason  Chang  and  Greg  Sharp.  This  work  was  supported  in  part  by  the  Na&onal  Alliance  for  Medical  Image  Compu&ng  (U54-­‐EB005149)  and  the  NeuroImaging  Analysis  Center  (P41-­‐EB015902).  

Introduc1on  •  Atlas-­‐based  segmenta&on  is  commonly  used  in  medical  image  analysis  •  Tendency  of  atlas-­‐based  segmenta&on  to  under-­‐segment  structures  •  Dice  overlap  and  Hausdorff  distance  do  not  measure  under-­‐segmenta&on  •  Wang,  Yushkevich  (2012)  proposed  deconvolu&on  of  label  maps  •  Contribu&ons:  

•  Volume  overlap  measures  to  quan&fy  under-­‐segmenta&on  •  Hypothesis:  asymmetry  in  single  organ  segmenta1on  as  cause    •  Genera&ve  model  of  background  to  correct  under-­‐segmenta&on  

Conclusions  •  Significant  bias  in  atlas-­‐based  segmenta&on  to  under-­‐segmenta&on  •  Asymmetry  in  foreground-­‐background  segmenta&on  as  new  hypothesis  •  Genera&ve  model  to  par&&on  background  reduces  under-­‐segmenta&on  

Under-­‐Segmenta1on  Measures  to  quan&fy  over-­‐  and  under-­‐segmenta&on:  

Results  •  Datasets:  •  16  heart  MRA  images  (0.6x0.6x1.5mm)  •  18  head  and  neck  CT  scans  (0.9x0.9x2.5mm)  •  39  brain  MRI  with  1mm  isotropic  resolu&on  

•  Comparison  to  deconvolu&on  (Wang,  2012)  •  Details:    •  DP-­‐GMM,  GMM,  DP-­‐means,  k-­‐means  •  Global  and  local  approach  •  Patch  size:  3  x  3  x  3  

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•  Quan&ta&ve  segmenta&on  results  for  three  applica&ons  •  Under-­‐segmenta&on  errors  significantly  higher  than  

over-­‐segmenta&on  errors   White  maDer  (WM),  Gray  maDer  (GM),  Hippocampus  (HC),  Caudate  (CA),    Putamen  (PU),  Pallidum  (PA),  Amygdala  (AM),  Accumbens  (AC),  Ventricles  (VE)  

First  column:  over-­‐segmenta&on  Second  column:  under-­‐segmenta&on  

Hypothesis  for  under-­‐segmenta1on  •  Under-­‐segmenta&on  is  caused  by  asymmetry    

in  foreground-­‐background  segmenta&on    •  Merging  all  surrounding  labels  into  background  creates  

a  new  meta-­‐label  that  dominates  the  vo&ng  process  •  Mul&-­‐organ  segmenta&on  of  brain  supports  hypothesis  

Latent  mul1-­‐label  model  of  the  background  •  Genera&ve  model  for  the  unsupervised  separa&on  of  the  

background  in  K  components  and  simultaneous  es&ma&on  of  K  •  Dirichlet  process  Gaussian  mixture  model  (DP-­‐GMM)  on  patches:  

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