Medical segmentation of optical coherence tomography (OCT) images using deep neural networks (DNNs) has been intensively studied in recent years, but generalization across datasets from different OCT devices is still a considerable challenge. In this work, we focus on the novel self-examination low-cost full-field (SELFF)- OCT, a handheld imaging device for home-monitoring of retinopathies, and the clinically used Spectralis-OCT. Images from both devices exhibit different characteristics, leading to different representations within DNNs and consequently to a reduced segmentation quality when switching between devices. To robustly segment OCT images from an OCT-scanner unseen during training, we alter the appearance of the images using manipulation methods ranging from traditional data augmentation to noise-based methods to learning-based style transfer methods. We evaluate the effect of the manipulation methods with respect to segmentation quality and changes in the feature space of the DNN. Reducing the domain shift with style transfer methods results in a significantly better segmentation of pigment epithelial detachment (PED). Investigations of the feature space show that the segmentation quality of PED is negatively correlated with the distance between training and test distributions. Our methods and results help researchers to choose and evaluate image manipulation methods for developing OCT segmentation models which are robust against domain shifts.
The assessment of lymph node metastases is critical for accurate cancer staging and consequently the decision for treatment options. Lymph node staging is a challenging, time-consuming task due to the fact that lymph nodes have ill-defined borders as well as varying sizes and morphological characteristics. The purpose of this study is to evaluate the effects of using different anatomical priors with the aim of guiding network attention within the application of segmentation of pathological lymph nodes in the mediastinum. The first presented prior, a distance map, displays the distance to a commonly defined point across all patients and, thus, provides an orientation of where a patch is extracted from. The second prior option, a probabilistic lymph node atlas, provides a map of areas where healthy and pathological lymph nodes are located, but also highlights lymph node stations that are more likely to become malignant. The distance map as well as the probabilistic lymph node atlas are results of an upstream atlas-to-patient registration approach. The third prior is a combination of segmentation masks of anatomical structures generated by the TotalSegmentator algorithm. A paired t-test on 5-fold cross validated results shows no significant differences in Dice score between models trained with the distance map or/and the probabilistic lymph node atlas compared to models trained with CT only. Counterintuitively, the models trained with segmentation masks of selected anatomical structures show significantly decreased segmentation accuracy. However, using the probabilistic lymph node atlas reduces the number of false negatives and consistently elevates the effect of post-processing.
The performance of a segmentation network optimized on data from a specific type of OCT sensor will decrease when applied to data from a different sensor. In this work, we deal with the research question of adapting models to data from an unlabeled new sensor with new properties in an unsupervised way. This challenge is known as unsupervised domain adaptation and can alleviate the need for costly manual annotation by radiologists. We show that one can strongly improve a model’s result that was trained in a supervised way on the source OCT sensor domain on the target sensor domain. We do this by aligning the source and target domain distributions in the feature space through a semantic clustering method. Apart from the unsupervised domain adaptation, we improved even the supervised training compared to the results in the RETOUCH challenge by employing a sophisticated training strategy. The RETOUCH challenge contains three different types of OCT scanners and provides annotations for the task of disease-related fluid classes.
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