shape modeling of the paranasal

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Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations Ayushi Sinha a, Simon Leonard a, Austin Reiter a, Masaru Ishii b, Russell H. Taylor a and Gregory D. Hager a a Dept. of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA b Dept. of Otolaryngology-Head and Neck Surgery, Johns Hopkins Medical Institutions, Baltimore, MD 21287, USA

Introduction Functional endoscopic sinus surgery (FESS) is a routine operation performed by an otolaryngologist Between 200,000 and 600,000 endoscopic interventions per year in the USA [1][2][3] Navigation during surgery can be improved using pre-operative CT Reduces likelihood of potential complications Enhances patient safety and outcome [1] Hosemann W, Draf C. Danger points, complications and medico-legal aspects in endoscopic sinus surgery. GMS Current Topics in Otorhinolaryngology, Head and Neck Surgery. 2013;12:Doc06. [2] Hepworth EJ, Bucknor M, Patel A, Vaughan WC. Nationwide survey on the use of image-guided functional endoscopic sinus surgery. Otolaryngol Head Neck Surg. 2006 Jul;135(1):68 73. [3] Psaltis AJ, Soler ZM, Nguyen SA, Schlosser RJ. Changing trends in sinus and septal surgery, 2007 to 2009. Int Forum Allergy Rhinol. 2012 Sep-Oct;2(5):357 361.

Nasal Cycle Alternating partial congestion and decongestion of the nasal cavities due to the expansion and contraction of the inferior, middle, and superior turbinates [4] Each cycle can span between ~50 minutes to several hours [5] Need to compensate for this regularly deforming topology [4] Hasegawa M, Kern EB, The human nasal cycle. Mayo Clinic Proceedings. May 1977;51:28-34 [5] Atanasov AT. Length of Periods in the Nasal Cycle during 24-Hours Registration. Open Journal of Biophysics. 2014;4:93-96

Can we estimate this deformation? Lack of longitudinal studies But, plenty of head CTs from different patients Can we characterize this deformation from head CTs of a large population?

Can we estimate this deformation? Hypothesis: Given CTs of n individuals, it is likely that the turbinates of each individual are at a different state in the nasal cycle than all others. Therefore, a statistical model of the turbinates built from these n CTs should also reflect natural variation.

Method Patient CTs Statistical Shape Model (SSM) Deformably Register Template Deformed Template Deform Template PCA

Template Creation [6] 1. Automatic Segmentation Template Creation Deformable Registration Segmentation Improvement 2. Statistical Shape Modeling Principal Component Analysis Correspondence Improvement [6] BB Avants, P Yushkevich, J Pluta, D Minko, M Korczykowski, J Detre, JC Gee, The optimal template effect in hippocampus studies of diseased populations," NeuroImage 49(3), p. 2457, 2010.

Automatic Segmentation [7] Deformation Fields Deformed Meshes 1. Automatic Segmentation Template Creation Deformable Registration Segmentation Improvement Template Mesh 2. Statistical Shape Modeling Principal Component Analysis Correspondence Improvement [7] BB Avants, NJ Tustison,. Song, PA Cook, A Klein, and JC Gee, A reproducible evaluation of ANTs similarity metric performance in brain image registration," NeuroImage 54(3), pp. 2033-2044, 2011.

Deformable Registration (DR) [7] 1. Automatic Segmentation Template Creation Deformable Registration Segmentation Improvement 2. Statistical Shape Modeling Principal Component Analysis Correspondence Improvement [7] BB Avants, NJ Tustison,. Song, PA Cook, A Klein, and JC Gee, A reproducible evaluation of ANTs similarity metric performance in brain image registration," NeuroImage 54(3), pp. 2033-2044, 2011.

Gradient Vector Flow (GVF) [10][11] 1. Automatic Segmentation Template Creation Deformable Registration Segmentation Improvement 2. Statistical Shape Modeling Principal Component Analysis Correspondence Improvement [10] C. Xu and J. L. Prince, Gradient vector ow: A new external force for snakes," in Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, pp. 66-71, 1997. [11] C. Xu and J. Prince, Snakes, shapes, and gradient vector flow," Image Processing, IEEE Transactions on 7, pp. 359-369, Mar 1998.

Segmentation Results Left Maxillary Sinus Right Maxillary Sinus DR GVF DR GVF Front Back

Segmentation Results Errors (mm) as compared to hand segmented ground truth

Segmentation Results Errors (mm) as compared to hand segmented ground truth

Statistical Shape Model (SSM) [8][9] 1. Automatic Segmentation Template Creation Deformable Registration Segmentation Improvement PCA 2. Statistical Shape Modeling Principal Component Analysis Correspondence Improvement [8] T. Cootes, C. Taylor, D. Cooper, and J. Graham, Active shape models-their training and application, Computer Vision and Image Understanding 61(1), pp. 38-59, 1995. [9] G. Chintalapani, L. M. Ellingsen, O. Sadowsky, J. L. Prince, and R. H. Taylor, Statistical atlases of bone anatomy: construction, iterative improvement and validation," in Medical Image Computing and Computer- Assisted Intervention, pp. 499-506, 2007.

Statistical Shape Model (SSM) Middle Turbinate Inferior Turbinate Right Maxillary Sinus Left Maxillary Sinus 1 st Mode 2 nd Mode

Correspondence Improvement [12] 1. Automatic Segmentation Template Creation Deformable Registration Segmentation Improvement 2. Statistical Shape Modeling Principal Component Analysis Correspondence Improvement [12] Seshamani S, Chintalapani G, Taylor RH, Iterative refinement of point correspondences for 3d statistical shape models. MICCAI. 2011;417-425.

Correspondence Improvement [12] 1. Automatic Segmentation Template Creation Deformable Registration Segmentation Improvement 2. Statistical Shape Modeling Principal Component Analysis Correspondence Improvement PCA [12] Seshamani S, Chintalapani G, Taylor RH, Iterative refinement of point correspondences for 3d statistical shape models. MICCAI. 2011;417-425.

Mean Vertex Error (mm) Residual Surface Error (mm) Leave-one-out Analysis Middle Turbinate: Vertex Error Middle Turbinate: Residual Surface Error 1.6 Iter_0 0.8 Iter_0 1.4 Iter_1 Iter_2 0.7 Iter_1 Iter_2 1.2 Iter_3 0.6 Iter_3 1 0.5 0.8 0.4 0.6 0.3 0.4 0.2 0.2 0.1 0 0 10 20 30 40 50 60 0 0 10 20 30 40 50 60 # modes # modes

Natural Variation Hypothesis: Given CTs of n individuals, it is likely that the turbinates of each individual are at a different state in the nasal cycle than all others. Therefore, a statistical model of the turbinates built from these n CTs should also reflect natural variation.

Natural Variation Experiment Pre-op CT b i I1 = m i T V I1 V n s V I1 = V + b I1 i m i PATIENT X Post-op CT i=1 b i I2 = m i T V I2 V n s V I2 = V + b I2 i m i Compare mode weights i=1 Built separate models for skull and inferior turbinates We expect inferior turbinates to change, but the skull to not change. This should be reflected in the mode weights when pre-op and postop models are projected onto our statistical model.

Mode weights Mode weights Natural Variation Mode Weights: Bone Mode Weights: Inferior Turbinate 4 4 3 3 2 2 1 1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20-1 -1-2 -2-3 -3-4 mode -4 mode P2a P2b P2a P2b

Difference Natural Variation Difference in Mode Weights 5 4.5 4 Bone IT 3.5 3 2.5 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 mode

Natural Variation

Natural Variation

Population variation vs Natural Variation

Summary We have built an initial statistical shape model (SSM) of the paranasal sinuses from CT scans of 53 different patients. SSMs of erectile tissue in the sinuses reflect variations due to the nasal cycle, which are captured in the modes of our PCA models. A preliminary experiment with a single same-patient pre-op/post-op CT image pair suggests that certain statistical modes are more sensitive than others in characterizing this variation.

Future Work We are currently working on constructing a larger statistical atlas of the sinuses based on CT scans of 500 patients. We hope to extend our exploration of the nasal cycle using a larger number of same-patient longitudinal studies. We are also working to incorporate our results into ongoing research on intraoperative video-ct registration.

References [1] Hosemann W, Draf C. Danger points, complications and medico-legal aspects in endoscopic sinus surgery. GMS Current Topics in Otorhinolaryngology, Head and Neck Surgery. 2013;12:Doc06. [2] Hepworth EJ, Bucknor M, Patel A, Vaughan WC. Nationwide survey on the use of image-guided functional endoscopic sinus surgery. Otolaryngol Head Neck Surg. 2006 Jul;135(1):68 73. [3] Psaltis AJ, Soler ZM, Nguyen SA, Schlosser RJ. Changing trends in sinus and septal surgery, 2007 to 2009. Int Forum Allergy Rhinol. 2012 Sep-Oct;2(5):357 361. [4] Hasegawa M, Kern EB, The human nasal cycle. Mayo Clinic Proceedings. 1977 May;51:28-34 [5] Atanasov AT. Length of Periods in the Nasal Cycle during 24-Hours Registration. Open Journal of Biophysics. 2014;4:93-96 [6] Avants BB, Yushkevich P, Pluta J, Minko D, Korczykowski, M, Detre J, Gee JC. The optimal template effect in hippocampus studies of diseased populations. NeuroImage;49(3):2457-2010. [7] Avants BB, Tustison NJ, Song G, Cook PA, Klein A, and Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage. 2011;54(3):2033-2044. [8] Xu C, Prince JL. Gradient vector flow: A new external force for snakes. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on. 1997;66-71. [8] Cootes T, Taylor C, Cooper D, Graham J. Active shape models-their training and application. Computer Vision and Image Understanding. 1995;61(1):38-59. [9] Chintalapani G, Ellingsen LM, Sadowsky O, Prince JL, and Taylor RH, Statistical atlases of bone anatomy: construction, iterative improvement and validation. MICCAI. 2007;499-506. [10] C. Xu and J. L. Prince, Gradient vector ow: A new external force for snakes," in Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, pp. 66-71, 1997. [11] Xu C, Prince JL. Snakes, shapes, and gradient vector flow. Image Processing, IEEE Transactions on. 1998 Mar;7:359-369. [12] Seshamani S, Chintalapani G, Taylor RH, Iterative refinement of point correspondences for 3d statistical shape models. MICCAI. 2011;417-425. [13] Lorensen WE, Cline HE, Marching cubes: A high resolution 3d surface construction algorithm. SIGGRAPH. 1987;63-169. [14] Delgado-Gonzalo R, Chenouard N, Unser M. Spline-based deforming ellipsoids for interactive 3D bioimage segmentation. Image Processing, IEEE Transactions on. 2013 Oct;22:3926-3940. [14] Weiler K, Edge-based data structures for solid modeling in curved-surface environments. Computer Graphics and Applications, 1985 Jan;5:21-40.

Thank you! Questions? Acknowledgement: This work is funded by NIH R01-EB015530: Enhanced Navigation for Endoscopic Sinus Surgery through Video Analysis.

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