Evaluation of a Hierarchical Anatomical Segmentation Approach in VISCERAL Anatomy Benchmarks

Oscar Jiménez-del-Toro Henning Müller University of Applied Sciences Western Switzerland (HES-SO)

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•  Motivation •  VISCERAL

•  Method

•  Experimental Setup •  Results Anatomy 1 Benchmark

•  Discussion

•  Conclusions

Overview

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•  Motivation •  VISCERAL

•  Method

•  Experimental Setup •  Results Anatomy 1 Benchmark

•  Discussion

•  Conclusions

Overview

•  Anatomical segmentation is fundamental for further image analysis and Computer-Aided Diagnosis1

•  Manual annotation and visual inspection is time consuming for radiologists

•  Accurate large scale data analysis techniques are needed

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Motivation

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•  Motivation •  VISCERAL

•  Method

•  Experimental Setup •  Results Anatomy 1 Benchmark

•  Discussion

•  Conclusions

Overview

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•  Motivation •  VISCERAL •  Method

•  Experimental Setup •  Results Anatomy 1 Benchmark

•  Discussion

•  Conclusions

Overview

VISual Concept Extraction ���challenge in RAdioLogy

•  EU funded project (2012-2015) – HES-SO, ETHZ, UHD, MUW, TUW, Gencat

•  Organize competitions on medical image analysis on big data o  Anatomy benchmarks

o  Detection benchmark

o  Retrieval benchmark

VISCERAL Anatomy Benchmarks •  All computations done in the

cloud

•  Annotation by medical doctors

•  Automatic segmentation of anatomical structures (20) and landmark detection

•  CT and MR images (contrast-enhanced and non-enhanced)

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•  Motivation •  VISCERAL

•  Method

•  Experimental Setup •  Results Anatomy 1 Benchmark

•  Discussion

•  Conclusions

Overview

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•  Motivation •  VISCERAL

•  Method •  Experimental Setup •  Results Anatomy 1 Benchmark

•  Discussion

•  Conclusions

Overview

Hierarchic Multi Atlas-Based Segmentation2 •  Use multiple atlases for label estimation •  Global and local alignment •  Hierarchical selection of the registrations

•  Reuse registrations from the bigger structures (eg. liver) for the smaller ones

•  Label fusion

•  Atlas = Patient volume + labels •  Coordinate transformation

increases spatial correlation between images3

•  Multi-scale gaussian pyramid4

Image registration

Affine alignment

•  Global rigid align

•  Independent local refinement for bigger structures (eg. liver, lungs)

•  Regions of interest based on the morphologically dilated initial estimations

•  Non-rigid b-spline

•  Multi-scale approach •  Faster optimization

due to better initial alignment

Non-rigid alignment

Label fusion

•  Majority voting threshold •  Classification on a per-voxel

basis •  Local registration errors are

reduced •  Threshold optimization

Right Kidney

Liver Urinary Bladder

Right Lung

Left Lung

1st Lumbar Vertebra

Gall- bladder

Left Kidney Trachea

Spleen 2nd Local

Affine

Hierarchical Registration

Affine

Local Affine

B-spline non-rigid Label fusion

Global alignment

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•  Motivation •  VISCERAL

•  Method

•  Experimental Setup •  Results Anatomy 1 Benchmark

•  Discussion

•  Conclusions

Overview

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•  Motivation •  VISCERAL

•  Method

•  Experimental Setup •  Results Anatomy 1 Benchmark

•  Discussion

•  Conclusions

Overview

Experimental setup

•  VISCERAL Anatomy 1 •  Testset :12 contrast-enhanced CT of the trunk

•  Applied to 10 anatomical structures

•  VISCERAL Anatomy 2 •  Testset :10 contrast-enhanced CT of the trunk

•  10 unenhanced whole body CT

•  Applied to ALL anatomical structures

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•  Motivation •  VISCERAL

•  Method

•  Experimental Setup •  Results Anatomy 1 Benchmark

•  Discussion

•  Conclusions

Overview

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•  Motivation •  VISCERAL

•  Method

•  Experimental Setup •  Results Anatomy 1 Benchmark •  Discussion

•  Conclusions

Overview

Anatomy 1 Results DICE coefficient

DICE coefficient

Top rank in benchmark

Anatomy 1 Results

Anatomy 1 Results Average distance error

Anatomy 1 Results Average distance error

Top rank in benchmark

Discussion

Comparison with other participant methods: -  SJ: Spanier et al. Rule-based approach with

region growing for multiple organs -  HJ: Huang et al. Multiple prior knowledge

models and free-form deformation -  W: Wang et al. Fast model bases level set

method and hierarchical shape priors -  K: Kazmig et al. Clustering and graph cut

using shortest path constraint for spatial relations

-  GG: Gass et al. Multiple atlases via Markov random field registrations

(DICE)  

Discussion

Comparison with other participant methods: -  SJ: Spanier et al. Rule-based approach with

region growing for multiple organs -  HJ: Huang et al. Multiple prior knowledge

models and free-form deformation -  W: Wang et al. Fast model bases level set

method and hierarchical shape priors -  K: Kazmig et al. Clustering and graph cut

using shortest path constraint for spatial relations

-  GG: Gass et al. Multiple atlases via Markov random field registrations

(DICE)  

Discussion •  Competitive results compared with up to 5

segmentation methods in Anatomy1

•  Similar to state-of-the-art methods for some organs: liver (0.89-0.96)5,6, kidneys (0.92-0.98)7,8

•  Segments not only abdominal organs but can be implemented for any anatomical structure

•  Future work: Extend to method to other modalities

Conclusion •  Straightforward automatic multi-structure

segmentation method

•  Showed robustness in multiple structures particularly for ceCT

•  High overlap for the bigger structures (e.g. liver, lungs) and competitive overlap for smaller structures (e.g. gallbladder)

Thank you for your attention !!

References 1 K.Doi. Current status and future potential of computer-aided diagnosis in medical imaging. British Journal of Radiology, 78:3-19, 2005 2Jiménez del Toro et al., Multi-structure Atlas-Based Segmentation using Anatomical Regions of Interest. Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI2013) MCV workshop, Nagoya, Japan, 2013 3Stefan Klein et al. Elastix: a toolbox for intensity-based medical image registration. IEEE Transactions on medical imaging, 29(1):196-205, 2010 4 Stefan Klein et al. Adaptive stochastic gradient descent optimisation for image registration. International Journal of Computer Vision, 81(3):227-239, 2009

References 5Criminisi et al. Regression forests for efficient anatomy detection and localization in computed tomography scans. Medical Image Analysis, 17(8):1293-1303, 2013 6Okada et al. Abdominal multi-organ segmentation of CT images based on hierarchical spatial modeling of organ interrelations. Abdominal Imaging 2011, 7029:173-180, 2012 7Zhou et al. Automatic localization of solid organs on 3D CT images by a collaborative majority voting decision based on ensemble learning. Computerized Medical Imaging and Graphics, 36:304-313, 2012 8Wolz et al. Multi-organ abdominal CT segmentation using hierarchically weighted subject-specific atlases. Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI2012), 7510:10-17, 2012