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Improving the Efficiency of Manual Ground Truth Labeling Using Automated Anatomy Segmentation Hongzhi Wang PhD, Prasanth Prasanna MD, Jose Morey MD, Tanveer F. Syeda-Mahmood PhD Medical Sieve Group, IBM Almaden Research Center

Anatomy Segmentation: labeling anatomical structures of interest in medical images • Corner stone for quantitative image analysis

Gold standard: manual segmentation Manual tracing in ITK-SNAP Manual tracing in Amira

Gold standard: manual segmentation Manual tracing in ITK-SNAP Manual tracing in Amira • Very time consuming, hours or even days to annotate a single 3D volume

Techniques for Assisting Manual Segmentation • Interactive manual segmentation enhanced with interpolation techniques, e.g. MITK and Amira • Only a subset of 2D slices are manually annotated • Annotation for the full 3D volume is generated through interpolation • Annotation time is a fraction of standard manual segmentation, depending on percentage of manually annotated slices • Semi-automatic segmentation, e.g. Pluta et al. 2009, Daisne & Blumhofer 2013 • Automatic segmentation produces initial annotation • Mistakes corrected by human experts • Most focusing on single anatomical structure • ~ 40% time reduction comparing to standard manual segmentation (Daisne & Blumhofer 2013)

Aims • Investigate full potential in time reduction by semi-automatic segmentation • Employing state of the art automatic anatomy segmentation algorithm • Challenging multi-structure segmentation task • cardiac CT anatomy segmentation with 20 anatomical structures • Comparison with interpolation-based interactive manual segmentation

Data Description • 33 cardiac CT studies o 28 cases used for training automatic segmentation o 5 testing cases for experimental validation • 20 structures studied o Bone: sternum, vertebrae o Artery/vein: pulmonary artery (left/right/trunk), aorta (root/ascending/arch/descending), Superior/inferior vena cava o Cardiac structure: Left/right ventricle/atrium, left ventricular myocardium o Valve : aortic valve, tricuspid valve, pulmonary valve, mitral valve

Experimental Setup • Semi-Automatic Segmentation: o Automatic segmentation by multi-atlas label fusion o Manual correction by one clinician (PP) using Amira commercial software (FEI Corporate, Hillsboro, Oregon USA) • Manual segmentation o produced by the same clinician one week after semi-automatic segmentation is finished using Amira with the interpolation technique

Overview for Automatic Anatomy Segmentation Training Training . . . Image 1 Image k Target Image Registration Registration and Warping and Warping Candidate Candidate Segmentation Segmentation Joint Label Fusion Initial Final Post Processing Segmentation Segmentation

Overview for Automatic Anatomy Segmentation Training Training . . . Image 1 Image k Target Image Registration Registration and Warping and Warping Multi-Atlas Label Fusion Candidate Candidate Segmentation Segmentation Joint Label Fusion Initial Final Post Processing Segmentation Segmentation

Multi-Atlas Label Fusion Atlases (Training anatomical volumes) Warped Atlases Given CT Scan registration Label Image Registration Fusion . . . . . . A t ti R lt

Automatic segmentation: Post processing • Remove small isolated segments • Ensure boundaries between right ventricle and pulmonary artery and boundaries between substructures of aorta do not cross axial planes. image multi-atlas segmentation after post processing after manual correction The colored anatomical structures are: sternum; right ventricle; pulmonary artery trunk; myocardium; aortic root; ascending aorta; descending aorta; left atrium; right atrium; vertebrae.

Results: Leave-One-Out Performance of Multi-Atlas Segmentation Inter-rater precision for non-valve structures Automatic Segmentation

Results: Overall Time Reduction 37% time reduction statistically significant with p<0.0001

Conclusions and Discussion • Multi-atlas anatomy segmentation is accurate enough to save time for manual segmentation, even with advanced interpolation tools • Without post processing, manual correction does not save time! • Manually correcting small, isolated segments is time consuming! • Having smooth segmentation is important for efficient manual correction!

Backup Material

Results: Ratio of Corrected Voxels

Results: Corrections are Heterogeneous with respect to Anatomical Structures