Extracting neuronal activity signals from microscopy recordings of contractile tissue using B-spline Explicit Active Surfaces (BEAS) cell tracking

Abstract

Ca2+ imaging is a widely used microscopy technique to simultaneously study cellular activity in multiple cells. The desired information consists of cell-specific time series of pixel intensity values, in which the fluorescence intensity represents cellular activity. For static scenes, cellular signal extraction is straightforward, however multiple analysis challenges are present in recordings of contractile tissues, like those of the enteric nervous system (ENS). This layer of critical neurons, embedded within the muscle layers of the gut wall, shows optical overlap between neighboring neurons, intensity changes due to cell activity, and constant movement. These challenges reduce the applicability of classical segmentation techniques and traditional stack alignment and regions-of-interest (ROIs) selection workflows. Therefore, a signal extraction method capable of dealing with moving cells and is insensitive to large intensity changes in consecutive frames is needed. Here we propose a b-spline active contour method to delineate and track neuronal cell bodies based on local and global energy terms. We develop both a single as well as a double-contour approach. The latter takes advantage of the appearance of GCaMP expressing cells, and tracks the nucleus’ boundaries together with the cytoplasmic contour, providing a stable delineation of neighboring, overlapping cells despite movement and intensity changes. The tracked contours can also serve as landmarks to relocate additional and manually-selected ROIs. This improves the total yield of efficacious cell tracking and allows signal extraction from other cell compartments like neuronal processes. Compared to manual delineation and other segmentation methods, the proposed method can track cells during large tissue deformations and high-intensity changes such as during neuronal firing events, while preserving the shape of the extracted Ca2+ signal. The analysis package represents a significant improvement to available Ca2+ imaging analysis workflows for ENS recordings and other systems where movement challenges traditional Ca2+ signal extraction workflows.

Calcium image analysis in the moving gut

“…Muscle contractions cause nonrigid, nonuniform movement significantly larger than the field of view 2,26 . While motion artifacts due to muscle contraction are often confined to the imaging plane, their nonuniformity renders obsolete rigid registration, and ganglia are inconsistently registered throughout the field of view 27 …”

“…Software and tools in this space are often closed source and either expensive or only available upon request from the initial authors 26 . Recently, cell tracking has advanced by employing B‐spline explicit active surfaces to track feature boundaries in time 27 . However, the double contour segmentation boundary‐tracking method is limited to applications with a clear nuclear shadow, which is non ubiquitous in enteric calcium imaging.…”

Efficacy Evaluation of Ultrasound with Active Contour Model for Hemodialysis in Children with Renal Failure

“…Introducing a neural network into medical image segmentation, which consists of a large number of parallel nodes, is realized by adjusting the connection relationship and connection weight between nodes. Correct selection in image feature extraction can greatly reduce the computational complexity and improve the overall performance of the segmentation algorithm [ 15 , 16 ]. Active contour model (ACM) combines the knowledge of physics, geometry, and approximation ethics and comprehensively utilizes the information of regions and boundaries to segment the target image.…”