Abstract:

Craniosynostosis is a congenital disease character-ized by the premature closure of one or multiple sutures of theinfant’s skull. For diagnosis, 3D photogrammetric scans are aradiation-free alternative to computed tomography. However,data is only sparsely available and the role of data augmentation for the classification of craniosynostosis has not yet beenanalyzed.In this work, we use a 2D distance map representation ofthe infants’ heads with a convolutional-neural-network-basedclassifier and employ a generative adversarial network (GAN)for data augmentation. We simulate two data scarcity scenar-ios with 15 % and 10 % training data and test the influence ofdifferent degrees of added synthetic data and balancing under-represented classes. We used total accuracy and F1-score as ametric to evaluate the final classifiers.For 15 % training data, the GAN-augmented dataset showedan increased F1-score up to 0.1 and classification accuracy upto 3 %. For 10 % training data, both metrics decreased. We present a deep convolutional GAN capable of creatingsynthetic data for the classification of craniosynostosis. Us-ing a moderate amount of synthetic data using a GAN showedslightly better performance, but had little effect overall. Thesimulated scarcity scenario of 10 % training data may havelimited the model’s ability to learn the underlying data distribution.

Abstract:

In laparoscopic liver surgery (LLS) image-guidednavigation systems could support the surgeon by providingsubsurface information such as the positions of tumors andvessels. For this purpose, one option is to perform a registra-tion of preoperative 3D data and 3D surface patches recon-structed from laparoscopic images. Part of an automatic 3Dregistration pipeline is the feature description, which takes intoaccount various geometric and spatial information. Since thereis no leading feature descriptor in the field of LLS, two featuredescriptors are compared in this paper: The Fast Point FeatureHistogram (FPFH) and Triple Orthogonal Local Depth Images(TOLDI). To evaluate their performance, three perturbationswere induced: varying surface patch sizes, spatial displace-ment, and Gaussian deformation. Registration was performedusing the RANSAC algorithm. FPFH outperformed TOLDIfor small surface patches and in case of Gaussian deformationsin terms of registration accuracy. In contrast, TOLDI showedlower registration errors for patches with spatial displacement.While developing a 3D-3D registration pipeline, the choice ofthe feature descriptor is of importance, consequently a carefulchoice suitable for the application in LLS is necessary.

Abstract:

Background: Craniosynostosis is a condition caused by the premature fusion of skull sutures, leading to irregular growth patterns of the head. Three-dimensional photogrammetry is a radiation-free alternative to the diagnosis using computed tomography. While statistical shape models have been proposed to quantify head shape, no shape-model-based classification approach has been presented yet. Methods: We present a classification pipeline that enables an automated diagnosis of three types of craniosynostosis. The pipeline is based on a statistical shape model built from photogrammetric surface scans. We made the model and pathology-specific submodels publicly available, making it the first publicly available craniosynostosis-related head model, as well as the first focusing on infants younger than 1.5 years. To the best of our knowledge, we performed the largest classification study for craniosynostosis to date. Results: Our classification approach yields an accuracy of 97.8 %, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry. Regarding the statistical shape model, we demonstrate that our model performs similar to other statistical shape models of the human head. Conclusion: We present a state-of-the-art shape-model-based classification approach for a radiation-free diagnosis of craniosynostosis. Our publicly available shape model enables the assessment of craniosynostosis on realistic and synthetic data.

Abstract:

In laparoscopic surgery image-guided navigation systems could support the surgeon by providing subsurface information such as the positions of tumors and vessels. For this purpose, one option is to perform a reliable registration of preoperative 3D data and a surface patch from laparo-scopic video data. A robust and automatic 3D-3D registration pipeline for the application during laparoscopic surgery has not yet been found due to application-specific challenges. To gain a better insight, we propose a framework enabling a qualitative and quantitative comparison of different registration approaches. The introduced framework is able to evaluate 3D feature descriptors and registration algorithms by generating and modifying synthetic data from clinical examples. Different confounding factors are considered and thus the reality can be reflected in any simplified or more complex way. Two exemplary experiments with a liver model, using the RANSAC algorithm, showed an increasing registration error for a decreasing size of the surface patch size and after introducing modifications. Moreover, the registration accuracy was dependent on the position and structure of the surface patch. The framework helps to quantitatively assess and optimize the registration pipeline, and hereby suggests future software improvements even with only few clinical examples. Clinical relevance - The introduced framework permits a quantitative and comprehensive comparison of different registration approaches which forms the basis for a supportive navigation tool in laparoscopic surgery.