Endoscopic navigation in the absence of CT imaging Ayushi shi - - PowerPoint PPT Presentation

Endoscopic navigation in the absence of CT imaging

Ayushi shi Sinha ha1,

, , Xingtong Liu1, Austin Reiter1, Masaru Ishii2,

Greg Hager1, Russ Taylor1

1The Johns Hopkins University, Baltimore, USA 2Johns Hopkins Medical Institutes, Baltimore, USA

Code: https://gi tps://github hub.c .com/ m/Ayus yushi hiSinha/ci Sinha/cisstICP tICP

sinha

nha@j @jhu hu.edu edu

Nasal endoscopy in the clinic

• G. Scadding et al., Diagnostic tools in Rhinology EAACI

position paper, Clinical and Translational Allergy, 1(2), 2011

• G. Scadding et al., Diagnostic tools in Rhinology EAACI

position paper, Clinical and Translational Allergy, 1(2), 2011

Navi viga gati tion

• n withou

hout addi diti tional

• nal to

tools

• ls

Nasal endoscopy in the clinic

Nasal endoscopy in the clinic

• G. Scadding et al., Diagnostic tools in Rhinology EAACI

position paper, Clinical and Translational Allergy, 1(2), 2011

Navi viga gati tion

• n withou

hout addi diti tional

• nal to

tools

• ls

Estimat mate e anat natomy

• my without

hout CT T scan an

Nasal endoscopy in the clinic

• G. Scadding et al., Diagnostic tools in Rhinology EAACI

position paper, Clinical and Translational Allergy, 1(2), 2011

Navi viga gati tion

• n withou

hout addi diti tional

• nal to

tools

• ls

Estimat mate e anat natomy

• my without

hout CT T scan an Assign ign confi fiden dence ce to to registr gistration ion

Nasal endoscopy in the clinic

• G. Scadding et al., Diagnostic tools in Rhinology EAACI

position paper, Clinical and Translational Allergy, 1(2), 2011

Navi viga gati tion

• n withou

hout addi diti tional

• nal to

tools

• ls

Estimat mate e anat natomy

• my without

hout CT T scan an Assign ign confi fiden dence ce to to registr gistration ion

Navigation without additional tools

Learning-based method

Liu, X. et al., "Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy", CARE Workship 2018

Nasal endoscopy in the clinic

• G. Scadding et al., Diagnostic tools in Rhinology EAACI

position paper, Clinical and Translational Allergy, 1(2), 2011

Navi viga gati tion

• n withou

hout addi diti tional

• nal to

tools

• ls

Estimat mate e anat natomy

• my without

hout CT T scan an Assign ign confi fiden dence ce to to registr gistration ion

Estimate anatomy without CT scan

• Build statistical shape models
• Principal component analysis
• Capture anatomical variation
• Deformable registration
• Optimize PCA model parameters
• Produce registration score

Estimate anatomy without CT scan

• Build statistical shape models
• Principal component analysis
• Capture anatomical variation
• Deformable registration
• Optimize PCA model parameters
• Produce registration score

• Variance:

Statistical shape models

• Given shapes,

, with correspondences, we can compute:

• Mean:

Statistical shape models

• Variance along the principal mode for middle turbinates

Statistical shape models

• Given a new shape,

, we can compute:

• Mode weights:
• Estimated shape:

Estimate anatomy without CT scan

• Build statistical shape models
• Principal component analysis
• Capture anatomical variation
• Deformable registration
• Optimize PCA model parameters
• Produce registration score

Deformable most likely point (D-IMLP)

Find R, t and a such that x is best aligned with a deformed y… Find s such that y deforms to fit x

y𝑗 ∈ Ψ X = {x𝑗}

𝑔(yi, s)

Find R, t and a such that x is best aligned with a deformed y… Find s such that y deforms to fit x

y𝑗 ∈ Ψ X = {x𝑗}

and such that the normal of y aligns with that of x 𝑔(yi, s), ,

Generalized deformable most likely oriented point (GD-IMLOP)

𝐰𝑗

(2)

𝐰𝑗

(3)

𝐰𝑗

(1)

𝜈𝑗

(1)

𝜈𝑗

(2)

𝜈𝑗

(3)

y𝑗

What is ?

18

Nasal endoscopy in the clinic

• G. Scadding et al., Diagnostic tools in Rhinology EAACI

position paper, Clinical and Translational Allergy, 1(2), 2011

Navi viga gati tion

• n withou

hout addi diti tional

• nal to

tools

• ls

Estimat mate e anat natomy

• my without

hout CT T scan an Assign ign confi fiden dence ce to to registr gistration ion

Did it work?

≈ 𝜓2 distribution ≈ 𝜓2 =

distribution

Chi-squar square e value lue Probabil abilit ity densi sity ty 𝜓2

𝑞 = Pr[E𝑞 < 𝜓2]

=

Did it work?

= ≤ ≤ =

Chi-squar square e value lue Probabil abilit ity densi sity ty 𝜓2

𝑞 = Pr[E𝑞 < 𝜓2]

Experiments

Leave-one-out

• # sample point: 3000
• Translational offset: [0, 10] mm
• Rotational offset: [0, 10] degrees
• Noise:
• 0.5 × 0.5 × 0.75mm3
• 10° (𝑓 = 0.5)
• Noise assumed:
• 1 × 1 × 2mm3
• 30° (𝑓 = 0.5)
• 𝑜𝐧 ∈ {0, 10, 20, 30, 40, 50}

Right nasal airway

Leave-one-out

Leave-one-out

Leave-one-out

Leave-one-out

TR TRE = 0.34 (± 0.03)mm mm

𝑞 = 0.95 (ve very confident) t)

Leave-one-out

𝑞 = 0.9975 (confid ident) t)

TR TRE = 0.62 (± 0.03)mm mm

Leave-one-out

𝑞 = 0.9999 (some mewhat hat confident) t)

TR TRE = 0.78 (± 0.04)mm mm

Leave-one-out

𝑞 = 0.999999 (low confidence) ce)

TR TRE = 0.80 (± 0.05)mm mm

Leave-one-out

remain inin ing (n (no

• confidence)

e)

TRE > 1 mm

In vivo

• 5 clinical sequences
• 3000 sample points
• Noise assumed:
• 1 × 1 × 2mm3
• 30° (𝑓 = 0.5)
• 𝑜𝐧 ∈ {0, 10, 20, 30, 40, 50}

Right nasal airway Dense reconstruction from video

In vivo

# registrations Residual error (mm) Max error (mm) Min error (mm)

All registrations 30/30

1.09 (±1.03) 4.74 0.50

Registrations that pass Ep test 27/30

0.76 (±0.14) 0.99 0.50

Registrations that pass Ep and Eo tests 12/30

0.78 (±0.07) 0.94 0.72

Conclusions and future work

• Navigation without additional tools
• Estimate anatomy without CT scan
• Assign confidence to registration
• Learn statistics from 1000s of CTs
• Use additional features
• Evaluate further on in vivo data

Code de: https://github.com/AyushiSinha/cisstICP Poster er: W-2

Acknow nowle ledgemen dgements ts: This work was funded by NIH R01-EB015530, NSF Graduate Research Fellowship Program, an Intuitive Surgical, Inc. fellowship, and JHU internal funds.

: : sinha@jhu.edu : @ItsAyushiSinha

Thank you!