Pathology Segmentation using Distributional Differences to Images of Healthy Origin

Fully supervised segmentation methods require a large training cohort of already segmented images, providing information at the pixel level of each image. We present a method to automatically segment and model pathologies in medical images, trained solely on data labelled on the image level as either healthy or containing a visual defect. We base our method on CycleGAN, an image-to-image translation technique, to translate images between the domains of healthy and pathological images. We extend the core idea with two key contributions. Implementing the generators as residual generators allows us to explicitly model the segmentation of the pathology. Realizing the translation from the healthy to the pathological domain using a variational autoencoder allows us to specify one representation of the pathology, as this transformation is otherwise not unique. Our model hence not only allows us to create pixelwise semantic segmentations, it is also able to create inpaintings for the segmentations to render the pathological image healthy. Furthermore, we can draw new unseen pathology samples from this model based on the distribution in the data. We show quantitatively, that our method is able to segment pathologies with a surprising accuracy being only slightly inferior to a state-of-the-art fully supervised method, although the latter has per-pixel rather than per-image training information. Moreover, we show qualitative results of both the segmentations and inpaintings. Our findings motivate further research into weakly-supervised segmentation using image level annotations, allowing for faster and cheaper acquisition of training data without a large sacrifice in segmentation accuracy

Quantification of spinal cord atrophy in magnetic resonance images

Quantifying the volume of the spinal cord is of vital interest for studying and understanding diseases of the central nervous system such as multiple sclerosis (MS). In this thesis, which is motivated by MS research, we propose methods for measuring the spinal cord cross-sectional area and volume in magnetic resonance (MR) images. These measurements are used for determining neural atrophy and for performing both longitudinal and cross-sectional comparisons in clinical trials. We present three evolutionary steps of our approach: In the first step, we use graph cut–based image segmentation on the intensities of T1-weighted MR images. In the second step, we combine a continuous max flow segmentation algorithm with a cross-sectional similarity prior and Hessian-based structural features, which we apply to T1- and T2-weighted images. The prior leverages the fact that the spinal cord is an elongated structure by constraining its cross-sectional shape to vary only slowly along one image axis. In conjunction with the additional features, the segmentation robustness is thus increased. In the third step, we combine continuous max flow with anisotropic total variation regularization, which enables us to direct the regularization of the cross-sectional shape of the spinal cord more flexibly. We implement the proposed approach as a semi-automatic software toolchain that automatically segments the spinal cord, reconstructs its surface, and acquires the desired measurements. The software employs a user-provided anatomical landmark as well as hints for the location of the spinal cord and its surroundings. It accounts for the bending of the spine, MR-induced image distortions, and noise. We evaluate the proposed methods in experiments on phantom, healthy subject, and patient data. Our measurement accuracy and precision are on par with the state of the art. At the same time, our measurements on MS patient data are in accordance with the medical literature

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation

Model-Driven 3-D Regularisation for Robust Segmentation of the Refractive Corneal Surfaces in Spiral {OCT} Scans

Measuring the cornea’s anterior and posterior refractive surface is essential for corneal topography, used for diagnostics and the planning of surgeries. Corneal topography by Optical Coherence Tomography (OCT) relies on proper segmentation. Common segmentation methods are limited to specific, B-scan-based scan patterns and fail when applied to data acquired by recently proposed spiral scan trajectories. We propose a novel method for the segmentation of the anterior and posterior refractive surface in scans acquired by 2-D scan trajectories – including but not limited to spirals. Key feature is a model-driven, three-dimensional regularisation of the region of interest, slope and curvature. The regularisation is integrated into a graph-based segmentation with feature-directed smoothing and incremental segmentation. We parameterise the segmentation based on test surface measurements and evaluate its performance by means of 18 in vivo measurements acquired by spiral and radial scanning. The comparison with expert segmentations shows successful segmentation of the refractive corneal surfaces

With Gaze Tracking Towards Noninvasive Eye Cancer Treatment

We present a new gaze tracking-based navigation scheme for proton beam radiation of intraocular tumors and we show the technical integration into the treatment facility. Currently, to treat a patient with such a tumor, a medical physicist positions the patient and the affected eye ball such that the radiation beam targets the tumor. This iterative eye positioning mechanism requires multiple X-rays, and radio-opaque clips previously sutured on the target eyeball. We investigate a possibility to replace this procedure with a noninvasive approach using a 3-D model-based gaze tracker. Previous work does not cover a comparably extensive integration of a gaze tracking device into a state-of-the-art proton beam facility without using additional hardware, such as a stereo optical tracking system. The integration is difficult because of limited available physical space, but only this enables to quantify the overall accuracy. We built a compact gaze tracker and integrated it into the proton beam radiation facility of the Paul Scherrer Institute in Villigen, Switzerland. Our results show that we can accurately estimate a healthy volunteer's point of gaze, which is the basis for the determination of the desired initial eye position. The proposed method is the first crucial step in order to make the proton therapy of the eye completely noninvasive

High order slice interpolation for medical images

In this paper we introduce a high order object- and intensity-based method for slice interpolation. Similar structures along the slices are registered using a symmetric similarity measure to calculate displacement fields between neighboring slices. For the intensity-based and curvature-regularized registration no manual landmarks are needed but the structures between two subsequent slices have to be similar. The set of displacement fields is used to calculate a natural spline interpolation for structural motion that avoids kinks. Along every correspondence point trajectory, again high order intensity interpolating splines are calculated for gray values. We test our method on an artificial scenario and on real MR images. Leave-one-slice-out evaluations show that the proposed method improves the slice estimation compared to piecewise linear registration-based slice interpolation and cubic interpolation