l User-guided Segmentation of Thoracic Computed Tomography...
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User-guided Segmentation of Thoracic Computed Tomography Data for Electrical Impedance Tomography Image Reconstruction Peter Salz * IEEE Student Member Andreas Reske † Hermann Wrigge Gerik Scheuermann ‡ IEEE Member Hans Hagen IEEE Member ABSTRACT Electrical Impedance Tomography (EIT) is a promising technique to visualize lung function, but it suffers from inaccurate thorax models. To easily generate such models in the presence of severe lung damage, we propose an interactive segmentation workflow that utilizes expert knowledge. Body shape, lung, ribs, heart and patho- logical lung tissue are segmented from CT scans to allow multi- material body models for EIT image reconstruction. The workflow includes histogram-based thresholding, body shape extraction, ribcage and heart segmentation and uses well- known techniques like Connected Component Analysis and mor- phological operations as well as interactive algorithms like Geodesic Segmentation. Our method can deal with both human and pig body geometry and to our knowledge there is no other applica- tion that features a similar integrated workflow with planned expert user interactions while having the same potential for multi-material thorax segmentation. Index Terms: I.4.6 [Image Processing and Computer Vision]: Segmentation—Pixel Classification 1 I NTRODUCTION Electrical Impedance Tomography Electrical Impedance Tomography (EIT) is a functional imaging technique that visualizes conductivity changes inside the body. It is now being used to monitor pulmonary gas distribution during mechanical ventilation of patients in intensive care [3]. The choice of body models is crucial for the quality of reconstructed images. The state of the art is a 2.5D model generated from a single slice segmentation [2]. Major problems of EIT are the low spatial resolution, noise and perfusion affecting the signal, poor image quality for patients that deviate significantly from the model (especially obese patients) and unclear correspondences of regions in EIT and CT. Goals The segmentation procedure presented here is intended to fos- ter patient-specific model generation from semi-automatic multi- material segmentation of CT data. It is expected that image qual- ity improves significantly and creating anatomical context for EIT becomes possible. Along with the individualization of EIT image reconstruction one of our long-term goals is to help justify the use- fulness of EIT by providing anatomical context to assess EIT image quality. * P. Salz and H. Hagen are with Computer Graphics and HCI Group, University of Kaiserslautern, Germany. email: [email protected] † A. Reske and H. Wrigge are with the Clinic and Policlinic for Anesthe- siology and Intensive Care Medicine, University Hospital Leipzig, Germany ‡ G. Scheuermann is with Institute for Computer Science, University of Leipzig Contributions The segmentation framework we propose fuses different algorithms and segmentation methods into an integrated process with planned user interactions to generate robust multi-material segmentations of thoracic CT data. Our method is currently intended to segment thorax shape, (aer- ated) lung shape, heart, ribs, descending aorta and metal electrodes in the presence of artifacts and severe pathologies (like atelectasis, pleural effusion or pneumothorax). These can also be segmented using an interactive scribbling segmentation method [1]. The main intended use for this segmentation is the generation of 3D models for EIT image reconstruction, but we also plan to use our method for segmentation of the huge amount of CT data that is recorded during animal studies [3]. A graphical overview of our proposed method is shown in Fig. 1. Slice Selection Anisotropic Diffusion Histogram Computation Preprocessing Automatic Initialization Manual Adaptation Initial Segmentation Intensity Thresholding Component Isolation Convex Hull Geodesic Segmentation Thorax Shape Extraction Inside / Outside Masks Bone Completion Outside Voxel Removal Material Adaptation Component Removal Inner Contour Points Final Ribcage Mask Ribcage Segmentation Organ Segmentation Morphological Isolation Contact Points Electrode Segmentation Geodesic Segmentation Figure 1: The workflow of our method. 2 SEGMENTATION PROCESS SUMMARY Initial Segmentation based on Histogram Thresholding After the user selects a start and end slice (to constrain the region of interest) and the data is smoothed using anisotropic diffusion (to reduce noise from metal artifacts), the log-histogram is com- puted. Several peaks represent structures like aerated lung, mus- cle/fat/blood tissue, bones and metal. An initial segmentation is
Transcript of l User-guided Segmentation of Thoracic Computed Tomography...
User-guided Segmentation of Thoracic Computed Tomography Data forElectrical Impedance Tomography Image Reconstruction
Peter Salz∗
IEEE Student MemberAndreas Reske† Hermann Wrigge Gerik Scheuermann‡
IEEE MemberHans HagenIEEE Member
ABSTRACT
Electrical Impedance Tomography (EIT) is a promising techniqueto visualize lung function, but it suffers from inaccurate thoraxmodels. To easily generate such models in the presence of severelung damage, we propose an interactive segmentation workflow thatutilizes expert knowledge. Body shape, lung, ribs, heart and patho-logical lung tissue are segmented from CT scans to allow multi-material body models for EIT image reconstruction.
The workflow includes histogram-based thresholding, bodyshape extraction, ribcage and heart segmentation and uses well-known techniques like Connected Component Analysis and mor-phological operations as well as interactive algorithms likeGeodesic Segmentation. Our method can deal with both human andpig body geometry and to our knowledge there is no other applica-tion that features a similar integrated workflow with planned expertuser interactions while having the same potential for multi-materialthorax segmentation.
Index Terms: I.4.6 [Image Processing and Computer Vision]:Segmentation—Pixel Classification
1 INTRODUCTION
Electrical Impedance Tomography
Electrical Impedance Tomography (EIT) is a functional imagingtechnique that visualizes conductivity changes inside the body. Itis now being used to monitor pulmonary gas distribution duringmechanical ventilation of patients in intensive care [3]. The choiceof body models is crucial for the quality of reconstructed images.The state of the art is a 2.5D model generated from a single slicesegmentation [2].
Major problems of EIT are the low spatial resolution, noise andperfusion affecting the signal, poor image quality for patients thatdeviate significantly from the model (especially obese patients) andunclear correspondences of regions in EIT and CT.
Goals
The segmentation procedure presented here is intended to fos-ter patient-specific model generation from semi-automatic multi-material segmentation of CT data. It is expected that image qual-ity improves significantly and creating anatomical context for EITbecomes possible. Along with the individualization of EIT imagereconstruction one of our long-term goals is to help justify the use-fulness of EIT by providing anatomical context to assess EIT imagequality.
∗P. Salz and H. Hagen are with Computer Graphics and HCI Group,University of Kaiserslautern, Germany. email: [email protected]
†A. Reske and H. Wrigge are with the Clinic and Policlinic for Anesthe-siology and Intensive Care Medicine, University Hospital Leipzig, Germany
‡G. Scheuermann is with Institute for Computer Science, University ofLeipzig
ContributionsThe segmentation framework we propose fuses different algorithmsand segmentation methods into an integrated process with planneduser interactions to generate robust multi-material segmentations ofthoracic CT data.
Our method is currently intended to segment thorax shape, (aer-ated) lung shape, heart, ribs, descending aorta and metal electrodesin the presence of artifacts and severe pathologies (like atelectasis,pleural effusion or pneumothorax). These can also be segmentedusing an interactive scribbling segmentation method [1].
The main intended use for this segmentation is the generation of3D models for EIT image reconstruction, but we also plan to useour method for segmentation of the huge amount of CT data that isrecorded during animal studies [3].
A graphical overview of our proposed method is shown in Fig. 1.
Slice Selection
Anisotropic Diffusion
Histogram Computation
Preprocessing
Automatic Initialization
Manual Adaptation
Initial Segmentation
Intensity Thresholding
Component Isolation
Convex Hull
Geodesic Segmentation
Thorax Shape Extraction
Inside / Outside Masks
Bone Completion
Outside Voxel Removal
Material Adaptation
Component Removal
Inner Contour Points
Final Ribcage Mask
Ribcage Segmentation Organ Segmentation
Morphological Isolation
Contact Points
Electrode Segmentation
Geodesic Segmentation
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Figure 1: The workflow of our method.
2 SEGMENTATION PROCESS SUMMARY
Initial Segmentation based on Histogram ThresholdingAfter the user selects a start and end slice (to constrain the regionof interest) and the data is smoothed using anisotropic diffusion(to reduce noise from metal artifacts), the log-histogram is com-puted. Several peaks represent structures like aerated lung, mus-cle/fat/blood tissue, bones and metal. An initial segmentation is
computed by searching for local minima between peaks. Thesethresholds can be adapted by the user with immediate visual feed-back.
Thorax Shape ExtractionThorax shape is very important for Electrical Impedance Tomog-raphy image reconstruction, but it also allows to remove voxelsoutside the thorax which otherwise would interfere with automaticmethods.
Morphological operations (majority and erosion filters) and Con-nected Component Analysis are applied to isolate the thorax struc-ture. Its convex hull is then refined by scribbling: Foreground andbackground scribbles are drawn by the user and then processed byGeodesic Segmentation [1]. The initial segmentation is then up-dated with the new knowledge using inside/outside masks.
Ribcage SegmentationThe next step is to delineate the volume enclosed by the ribcagesince important structures like aerated and pathological lung tissue,heart and aorta are located there. The user selects undesired com-ponents (e.g. metal objects inside the body), and if they cannot beproperly separated by Connected Component Analysis, the user candraw regions that are then removed from the mask.
We locate those points on the contours inside the ribs’ convexhull that point towards its centroid. These points form the innerribcage region which is complemented with the lung mask to com-plete the region of interest.
Segmentation by ScribblingThe heart is the largest non-lung structure inside the ribcage. It isconnected to the thoracic wall and to the blood vessels, and some-times touches the diaphragm. We draw scribbles inside the heartand also place background scribbles at undesired structures (seeFig. 2a). Geodesic Segmentation [1] then produces quite successfulresults from scribbles in very few slices (usually just one is suffi-cient).
The procedure is repeated for the descending aorta and for at-electatic or otherwise non-aerated lung tissue (Fig. 2b). Note thatthis last segmentation is almost impossible to achieve automaticallyand is therefore usually done by manual contour drawing.
(a) Successful heart segmentation(green) with four scribbles.
(b) Successful separation of at-electasis (green) from other tis-sue.
Figure 2: Scribble-based segmentation of inner-thoracic structures.
Electrode SegmentationSince our pig data was recorded during animal studies with com-bined CT and EIT images we also have the opportunity to locatethe 16 electrode positions explicitly. Knowing the electrode plane,its angle towards the transverse plane and the exact positions on theskin is very crucial for EIT image reconstruction and helps reduc-ing artifacts significantly. We compute the contact location with the
skin as well as the electrode’s orientation with respect to the skinboundary.
3 RESULTS
Figure 3: Segmentation results and 3D visualization for twodatasets from a pig (top) and a human (bottom)
Even though the implementation of our method is at an earlystage, it works quite stable and delivers almost satisfactory results(in terms of model building for Electrical Impedance Tomography).
Fig. 3 (top) shows a pig dataset with partially atelectatic lung tis-sue and electrodes. In Fig. 3 (bottom) a human dataset with distinc-tive atelectatic lung tissue (light blue) is presented. The lung alsocontains a pneumothorax (purple) that is detected automatically dueto its special characteristics.
Note that all of our results were computed without manually trac-ing any material boundaries (which still is the state of the art in clin-ical practice). This produces enormous time savings and minimizesthe time a radiologist needs to deal with the data.
4 CONCLUSION
We presented our first results on semi-automatically segmentingthoracic CT data to generate patient-specific body models for EITimage reconstruction. We showed that even with very little user in-taction, quite good results can be achieved, while the planned inputof expert knowledge is crucial to the success.
We are currently conducting an evaluation comparing manuallysegmented lung data to our results in order to show the faster execu-tion time of our method while maintaining the same high quality ofsegmentation. We expect that our medical partners will also reportbetter usability than other (semi-)automatic methods.
REFERENCES
[1] X. Bai and G. Sapiro. A geodesic framework for fast interactive imageand video segmentation and matting. In Computer Vision, 2007. ICCV2007. IEEE 11th International Conference on, pages 1–8, 2007.
[2] D. Ferrario, A. Adler, J. Sola, S. Bohm, and M. Bodenstein. Unsuper-vised localization of heart and lung regions in EIT images: a validationstudy. Conf EIT, pages 3–6, 2011.
[3] T. Muders, H. Luepschen, J. Zinserling, S. Greschus, R. Fimmers,U. Guenther, M. Buchwald, D. Grigutsch, S. Leonhardt, C. Putensen,and H. Wrigge. Tidal recruitment assessed by electrical impedance to-mography and computed tomography in a porcine model of lung injury.Crit Care Med, 40(3):903–11, 2012.
