Fig 1.
Complete workflow of the segmentation and analysis of time-resolved soft X-ray tomograms (SXT).
Blue boxes highlight the three different frameworks: semantic segmentation, instance segmentation, and systematic analysis. Dark orange boxes show the steps of each framework; light orange boxes represent data at different stages; black arrows indicate the direction of data processing; and the thick orange arrow represents multiple and simultaneous tomograms processing. The predicted 2D labels of ‘cell’, ‘nucleus’, ‘mitochondria’ and individual ‘insulin vesicle’ masks were combined to generate 3D organelle masks. Then these 3D masks were merged together based on the priority (Method: Systematic analysis) to get the multi-organelle mask.
Fig 2.
Example of auto-segmentation and performance on the test dataset.
(A1-A2) 3D visualization to represent labels for Cell ID 766_8: (A1) manually segmented labels and (A2) auto-segmented labels. (B1-B3) Cropped 2D orthoslice of raw soft X-ray tomogram for Cell ID 842_17. Red box shows two vesicles near the plasma membrane (B1). Manually segmented mask where two vesicles are merged into one (B2). Auto-segmented mask showing correct prediction based on instance segmentation (B3). Each color represents a single instance. (C1-C3) 2D orthoslice of single insulin vesicle instance from soft X-ray tomogram of Cell ID 842_17. The voxel with the highest linear absorption coefficient (LAC) value was assigned as the center of the vesicle. LAC map of the vesicle and surrounding pixel (C1). Distance map from vesicle boundary (C2). Average LAC vs. distance of the vesicle instance from C2 shown as “Single insulin vesicle” (C3). Average LAC distribution for 3 test cells is also plotted in C3.
Table 1.
Auto-segmentation accuracy: Dice, Recall, AP50, and insulin vesicle numbers on test datasets compared to manual segmentation [3].
Table 2.
Linear absorption coefficient (LAC) value and normalized intensity of insulin vesicle and mitochondria masks in the three test datasets from the manual segmentation results.
Table 3.
Table 2 continuous: Linear absorption coefficient (LAC) value and normalized intensity of insulin vesicle mask in the three test datasets for those missed insulin vesicles masks.
Fig 3.
Insulin vesicle distribution and functional regions related to the insulin secretion pathway.
Radial distribution function (RDF) of insulin vesicles from the nuclear membrane under the given treatment conditions. Functional spaces related to the insulin secretion pathway are shown with different background colors as indicated. The light gray horizontal line at g(r) = 1.0 shows where the probability of finding insulin vesicles in a shell is the same as random probability.
Fig 4.
Radial distribution function (RDF) of insulin vesicle, mitochondria, and insulin vesicle-mitochondria contact under the various treatment conditions.
(A1-A3) RDF distributions under glucose and Ex-4 treatment conditions compared to glucose treatment/normal condition. (B1-B3) RDF distributions under glucose and NN414 treatment conditions compared to glucose treatment/NN414 treatment/KCl treatment. Standard deviations are marked at each point to represent bias for datasets in the same condition.