Thanuja D. Ambegoda

Dilith Jayakody, Thanuja Ambegoda

The task of segmentation of multispectral images, which are images with numerous channels or bands, each capturing a specific range of wavelengths of electromagnetic radiation, has been previously explored in contexts with large amounts of labeled data. However, these models tend not to generalize well to datasets of smaller size. In this paper, we propose a novel approach for improving few-shot segmentation performance on multispectral images using reinforcement learning to generate representations. These representations are generated as mathematical expressions between channels and are tailored to the specific class being segmented. Our methodology involves training an agent to identify the most informative expressions using a small dataset, which can include as few as a single labeled sample, updating the dataset using these expressions, and then using the updated dataset to perform segmentation. Due to the limited length of the expressions, the model receives useful representations without any added risk of overfitting. We evaluate the effectiveness of our approach on samples of several multispectral datasets and demonstrate its effectiveness in boosting the performance of segmentation algorithms in few-shot contexts. The code is available at https://github.com/dilithjay/IndexRLSeg.

Sathursan Kanagarajah, Thanuja Ambegoda, Ranga Rodrigo

Class Incremental Learning (CIL) is inspired by the human ability to learn new classes without forgetting previous ones. CIL becomes more challenging in real-world scenarios when the samples in each incremental step are imbalanced. This creates another branch of problem, called Generalized Class Incremental Learning (GCIL) where each incremental step is structured more realistically. Grow When Required (GWR) network, a type of Self-Organizing Map (SOM), dynamically create and remove nodes and edges for adaptive learning. GWR performs incremental learning from feature vectors extracted by a Convolutional Neural Network (CNN), which acts as a feature extractor. The inherent ability of GWR to form distinct clusters, each corresponding to a class in the feature vector space, regardless of the order of samples or class imbalances, is well suited to achieving GCIL. To enhance GWR's classification performance, a high-quality feature extractor is required. However, when the convolutional layers are adapted at each incremental step, the GWR nodes corresponding to prior knowledge are subject to near-invalidation. This work introduces the Self Augmenting Task Hallucinal Unified Representation (SATHUR), which re-initializes the GWR network at each incremental step, aligning it with the current feature extractor. Comprehensive experimental results demonstrate that our proposed method significantly outperforms other state-of-the-art GCIL methods on CIFAR-100 and CORe50 datasets.

Thanuja D. Ambegoda, Matthew Cook

We present a method for segmenting neuron membranes in 2D electron microscopy imagery. This segmentation task has been a bottleneck to reconstruction efforts of the brain's synaptic circuits. One common problem is the misclassification of blurry membrane fragments as cell interior, which leads to merging of two adjacent neuron sections into one via the blurry membrane region. Human annotators can easily avoid such errors by implicitly performing gap completion, taking into account the continuity of membranes. Drawing inspiration from these human strategies, we formulate the segmentation task as an edge labeling problem on a graph with local topological constraints. We derive an integer linear program (ILP) that enforces membrane continuity, i.e. the absence of gaps. The cost function of the ILP is the pixel-wise deviation of the segmentation from a priori membrane probabilities derived from the data. Based on membrane probability maps obtained using random forest classifiers and convolutional neural networks, our method improves the neuron boundary segmentation accuracy compared to a variety of standard segmentation approaches. Our method successfully performs gap completion and leads to fewer topological errors. The method could potentially also be incorporated into other image segmentation pipelines with known topological constraints.

Thanuja D. Ambegoda, Julien N. P. Martel, Jozef Adamcik et al.

Serial section electron microscopy (ssEM) is a widely used technique for obtaining volumetric information of biological tissues at nanometer scale. However, accurate 3D reconstructions of identified cellular structures and volumetric quantifications require precise estimates of section thickness and anisotropy (or stretching) along the XY imaging plane. In fact, many image processing algorithms simply assume isotropy within the imaging plane. To ameliorate this problem, we present a method for estimating thickness and stretching of electron microscopy sections using non-parametric Bayesian regression of image statistics. We verify our thickness and stretching estimates using direct measurements obtained by atomic force microscopy (AFM) and show that our method has a lower estimation error compared to a recent indirect thickness estimation method as well as a relative Z coordinate estimation method. Furthermore, we have made the first dataset of ssSEM images with directly measured section thickness values publicly available for the evaluation of indirect thickness estimation methods.