Improved Topological Preservation in 3D Axon Segmentation and Centerline Detection using Geometric Assessment-driven Topological Smoothing (GATS)

Automated axon tracing via fully supervised learning requires large amounts of 3D brain imagery, which is time consuming and laborious to obtain. It also requires expertise. Thus, there is a need for more efficient segmentation and centerline detection techniques to use in conjunction with automated annotation tools. Topology-preserving methods ensure that segmented components maintain geometric connectivity, which is especially meaningful for applications where volumetric data is used, and these methods often make use of morphological thinning algorithms as the thinned outputs can be useful for both segmentation and centerline detection of curvilinear structures. Current morphological thinning approaches used in conjunction with topology-preserving methods are prone to over-thinning and require manual configuration of hyperparameters. We propose an automated approach for morphological smoothing using geometric assessment of the radius of tubular structures in brain microscopy volumes, and apply average pooling to prevent over-thinning. We use this approach to formulate a loss function, which we call Geo-metric Assessment-driven Topological Smoothing loss, or GATS. Our approach increased segmentation and center-line detection evaluation metrics by 2%-5% across multiple datasets, and improved the Betti error rates by 9%. Our ablation study showed that geometric assessment of tubular structures achieved higher segmentation and centerline detection scores, and using average pooling for morphological smoothing in place of thinning algorithms reduced the Betti errors. We observed increased topological preservation during automated annotation of 3D axons volumes from models trained with GATS.

Developing a Series of AI Challenges for the United States Department of the Air Force

Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have influenced organizations such as the United States Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative between the DAF and MIT to bridge the gap between AI researchers and DAF mission requirements. Several projects supported by the DAF-MIT AI Accelerator are developing public challenge problems that address numerous Federal AI research priorities. These challenges target priorities by making large, AI-ready datasets publicly available, incentivizing open-source solutions, and creating a demand signal for dual use technologies that can stimulate further research. In this article, we describe these public challenges being developed and how their application contributes to scientific advances.

Self-Supervised Feature Extraction for 3D Axon Segmentation

Existing learning-based methods to automatically trace axons in 3D brain imagery often rely on manually annotated segmentation labels. Labeling is a labor-intensive process and is not scalable to whole-brain analysis, which is needed for improved understanding of brain function. We propose a self-supervised auxiliary task that utilizes the tube-like structure of axons to build a feature extractor from unlabeled data. The proposed auxiliary task constrains a 3D convolutional neural network (CNN) to predict the order of permuted slices in an input 3D volume. By solving this task, the 3D CNN is able to learn features without ground-truth labels that are useful for downstream segmentation with the 3D U-Net model. To the best of our knowledge, our model is the first to perform automated segmentation of axons imaged at subcellular resolution with the SHIELD technique. We demonstrate improved segmentation performance over the 3D U-Net model on both the SHIELD PVGPe dataset and the BigNeuron Project, single neuron Janelia dataset.