Application of Biomedical Digital Solutions: Virtual Flow Cytometry and Hematometrics in Pathology and Research

Transcription

1 Application of Biomdical Digital Solutions: Virtual Flow Cytomtry and Hmatomtrics in Pathology and Rsarch Hrnani D Cualing IHCFLOW Inc Lutz, FL, USA

2 Can ths diagnostic imags bcom data instad of just picturs? Can w go byond yballs or stimat? CD8 pankratin Brast Tumor infiltrating lymphocyts Brightfild and fluorscnt Modls: Routin HE tissu stain of bon marrow, rticulin fibrs stain for fibrosis in bon marrow and fluorscnt probs in clls

3 Futur of halthcar

4 Approach to Sgmntation Goals A good sgmntation is typically on which satisfis th usr rquirmnts, fits th modl systm for wid gnralization, fast implmntation and applicability: App -fits my workflow as pathologist, rally fast - adapt to th htrognity of imags by using th common thrad from ths imags, no mattr how diffrnt - should b multidisciplinary, as collaboration btwn scintists, mdical prsons, and computr nginrs.

5 Imag Data: common thrad? Rd,Blu, Grn channls contain all th information on objcts. Hu, saturation and Brightnss channls also hav usful information on all ths objcts Combination of abov channls allow application in a wid varity of tissu of diffrnt tissu stains using standard sgmntation bionginring tools in a dfind ordrd squnc

6 Pixls Light Stain Histograms light hu light intnsity light saturation 5 0 Distribution 6

7 Basic color of tissu stains and thir H, S, I histograms 7

8 Workshop paradigm W cratd a workshop paradigm : st of tools in th workshop usd in a dfind squnc, i bst chisls, plans, tools, as analogy for xampl, to convrt Planks of Honduras Mahogany to a Qun Ann tabl. W prmis a stratgy of using a piplin of primitiv, familiar, but rliabl sgmntation thrsholding and objct partitioning tools using th workshop modl: thrsholding histogram by intrmans or max ntropy, bit or grayscal masks, Watrshd, Voronoi tsslations, Cntroids, Math morphology, imag math Will lad to robust APPS in any numbr of targt antigns or tissu (bon marrow, stroma or any biomarkrs,tc ) And handl brightfild or fluorscnt imags

9 An app should b vrsatil, and basd on a workshop paradigm: w calld hmatomtrics, by using only a limitd st of good sgmntation tools applid to widly diffrnt tissu matrials to crat customizd final sgmntation rsults. CD8 pankratin Brast Tumor infiltrating lymphocyts Brightfild and fluorscnt Modls: Routin HE tissu stain of bon marrow, rticulin fibrs stain for fibrosis in bon marrow and fluorscnt probs in clls

10 RGB split g r n n r d Lymphocyt(CD8) VIRTUALFLOW STRATEGY C Rd (A) Blu(B) masks AorB b l u C mask ovrlay H mask HSB split h u Thrsh hu of C= D C xor D = E D Or F = G mask BRIGHLNESS Thrsh bright on E= F SATURATION

11 RGB split grnn Tumor infiltrating VIRTUALFLOW TUPLESC Rd (A) Blu(B) masks rd AorB blu C mask ovrlay H mask HSB split hu Thrsh hu of C= D C xor D = E D Or F = G mask brightnss Thrsh bright on E= F saturation

12 RGB split b l u Tumor Prcntag Rd (A) Blu(B) masks STRATEGY C r d AorB g r n n C mask ovrlay H mask HSB split Thrsh hu of C= D C xor D = E D Or F = G mask BRIGHLNESS Thrsh bright on E= F SATURATION

13 RGB split g r n n r d ENTROPHY BONE MARROW CELLULARITY Hmatomtrics STRATEGY Rd (A) Blu(B) masks AorB C b l u ENTROPHY C mask ovrlay H mask ISODATA HSB split Thrsh hu of C= D C xor D = E D Or F = G mask BRIGHLNESS SATURATION Thrsh bright on E= F 32 % marrow ovr fat clls

14 RGB split Bon Marrow fibrosis Hmatomtrics STRATEGY C Rd (D) Bright(F) masks rd DorF GrS Thrshbright of C= F E C mask ovrlay H mask HSB split BRIGHLNESS SATURATION

15 RGB split g r n n r d Confocal fluorscnc STRATEGY Rd (A) Blu(B) masks AorB C mask ovrlay C b l u Targt nucli HSB split Thrsh hu of C= D C xor D = E Targt intracllular probs BRIGHLNESS H and G SATURATION

16 Tissu microarray virtual flow High throughput analysis Corrsponding virtualflow tissu cytomics 16

17 Ara undr th ROC curv Ara Std. Error % confidnc to intrval P valu < Ara undr th ROC curv Ara Std. Error % confidnc intrval to P valu

18 Program Rsults dmonstrat positiv strong corrlation btwn th manual grading and computr grading using our tst and control groups. Th corrlation was high with r (nonparamtric Sparman p<0.0001) r = r 2 = p< Pathologist

19 Rsults dmonstrat positiv strong corrlation btwn th manual grading and computr grading using our tst and control groups. In th tst group, th man rticulin manual grading is 1.8 ( 95% CI ) vs AUTORETIC man of with 95% CI( ) with no significant diffrnc btwn th man( SD). Th corrlation was high with r 0.98 (nonparamtric Sparman p<0.0001). MANUAL == 2.5 AUTO RETIC = 3.0 MANUAL == 2 AUTO RETIC = 2.0

20 Virtual Flow cytomtry and hmatomtrics in Pathology and rsarch So in ths limitd xampls, data could b obtaind. Is this a bttr way than just yballing and stimating mtrics in vryday pathology work?

21 High disagrmnt without us of computrizd imag analysis. Biomarkr grading 1+ to 4+ of sam slids to 10 pathologists without and with computr aid Gaussian in log x scal Th low agrmnt is not th fault of pathologists, but lis from th limitation of our visual systm W ar good with linar scals but th brown staining ar in logarithmic scal from 0 to 1 log

22 Limitation of th human y 1. Rsolution is limitd to 0.1 mm 2. Limitd prcption of intnsity diffrnc (can diffrntiat up to 40 grays ) in linar scal 3. Inability to sparat hu from othr componnts or suscptibl to surround illusion 4. Prciv fals contours and illusions 22

23 How Virtual Flow/ Hmatomtrics diffrs from most commrcially imag analysis packags availabl?. ACIS Virtual Flow Most othr systms ar pixl and color ratio basd, for antign dnsity Virtual Flow/ Hmatomtrics is mor suitd to cll vnts counting, with ratios basd on singl clls or intracllular singl clls.

24 Comparison with ara analysis 52% objcts ara

25 Hodgkin lymphoma Ara prcnt imag analysis vs Positiv and Ngativly Staind Singl cll Dtction Sampl intrnt obtaind imag and ara-basd rsult (Company X) Comparison rsult using our cytomics approach

26 Virtual Flow Hmatomtrics will hlp in automating counting tissu markrs or putting data on tissu as mtrics. It is counting cll vnts, or subcllular objcts or any targt objcts. Lymph nod markd with anti CD8 or T supprssor 5 um Tissus ar sctions of clls that hav 8 to 32 microns cll diamtr

27 DIGITAL PATHOLOGY AND VIRTUAL FLOW CYTOMETRY WORKFLOW INTEGRATION WITH EASE EASY ACCESS TO AN APPLICATION LOW COST OF OWNERSHIP STANDARDIZED RESULTS AND UNIFIED EXPERIENCE VFLOW + STORE+ SHARING + NETWORK + DATABASE = APPS := VIRTUAL FLOW CYTOMETRY SMART PHONE(sp) := WHOLE SLIDE IMAGING

28 App: Traditional vs Computational Molcular Pathology Imag analysis cancr %= 43 BRAF and KRAS DNA squncing rquirs at last 20% tumor in tissu- thn a go for Sangr or Nxt gnration. DNA squncing

29 Virtual flow app

30 RESEARCH APP BIOMARKERS NOT ANTIGEN QUANT For CLIA LAB DIAGNOSTICS Or TISSUE METRICS Works with any microscop with any CCD

31 Virtual Flow cytomtry in Pathology Thr is a bttr way to just yballing and stimating mtrics in vryday pathology imags using digital pathology and virtual flow cytomtry. Virtual Flow dos tru counting with ratios basd on singl clls, th positiv ovr total clls ( positiv and ngativ clls) to gnrat a dual population dot plot paramtric displays which is nxt gnration abov ara analysis. Th virtual flow xtnd to hmatomtrics or to oncology mtrics using a novl workshop paradigm applicabl to wid varity of any color digital imags to crat custom final sgmntation rsults to gt data from picturs and to push traditional pathology to a nxt gnration computational pathology.

32 Computational Pathology Th advancs may hlp in augmnting traditional pathology into computational pathology in lin with prcision mdicin and gnomics.

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