Christian Wojek

Automated Retinal Image Analysis for Evaluation of Focal Hyperpigmentary Changes in Intermediate Age-Related Macular Degeneration

Purpose To develop and evaluate a software tool for automated detection of focal hyperpigmentary changes (FHC) in eyes with intermediate age-related macular degeneration (AMD). Methods Color fundus (CFP) and autofluorescence (AF) photographs of 33 eyes with FHC of 28 AMD patients (mean age 71 years) from the prospective longitudinal natural history MODIAMD-study were included. Fully automated to semiautomated registration of baseline to corresponding follow-up images was evaluated. Following the manual circumscription of individual FHC (four different readings by two readers), a machine-learning algorithm was evaluated for automatic FHC detection. Results The overall pixel distance error for the semiautomated (CFP follow-up to CFP baseline: median 5.7; CFP to AF images from the same visit: median 6.5) was larger as compared for the automated image registration (4.5 and 5.7; P < 0.001 and P < 0.001). The total number of manually circumscribed objects and the corresponding total size varied between 637 to 1163 and 520,848 pixels to 924,860 pixels, respectively. Performance of the learning algorithms showed a sensitivity of 96% at a specificity level of 98% using information from both CFP and AF images and defining small areas of FHC (“speckle appearance”) as “neutral.” Conclusions FHC as a high-risk feature for progression of AMD to late stages can be automatically assessed at different time points with similar sensitivity and specificity as compared to manual outlining. Upon further development of the research prototype, this approach may be useful both in natural history and interventional large-scale studies for a more refined classification and risk assessment of eyes with intermediate AMD. Translational Relevance Automated FHC detection opens the door for a more refined and detailed classification and risk assessment of eyes with intermediate AMD in both natural history and future interventional studies.

Cell Event Detection in Phase-Contrast Microscopy Sequences from Few Annotations

We study detecting cell events in phase-contrast microscopy sequences from few annotations. We first detect event candidates from the intensity difference of consecutive frames, and then train an unsupervised novelty detector on these candidates. The novelty detector assigns each candidate a degree of surprise. We annotate a tiny number of candidates chosen according to the novelty detector’s output, and finally train a sparse Gaussian process (GP) classifier. We show that the steepest learning curve is achieved when a collaborative multi-output Gaussian process is used as novelty detector, and its predictive mean and variance are used together to measure the degree of surprise. Following this scheme, we closely approximate the fully-supervised event detection accuracy by annotating only 3% of all candidates. The novelty detector based annotation used here clearly outperforms the studied active learning based approaches.

Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures

In this work we propose a novel framework for generic event monitoring in live cell culture videos, built on the assumption that unpredictable observations should correspond to biological events. We use a small set of event-free data to train a multioutput multikernel Gaussian process model that operates as an event predictor by performing autoregression on a bank of heterogeneous features extracted from consecutive frames of a video sequence. We show that the prediction error of this model can be used as a probability measure of the presence of relevant events, that can enable users to perform further analysis or monitoring of large-scale non-annotated data. We validate our approach in two phase-contrast sequence data sets containing mitosis and apoptosis events: a new private dataset of human bone cancer (osteosarcoma) cells and a benchmark dataset of stem cells.