CDeep3M-Plug-and-Play cloud-based deep learning for image segmentation

Abstract

As biomedical imaging datasets expand, deep neural networks are considered vital for image processing, yet community access is still limited by setting up complex computational environments and availability of high-performance computing resources. We address these bottlenecks with CDeep3M, a ready-to-use image segmentation solution employing a cloud-based deep convolutional neural network. We benchmark CDeep3M on large and complex two-dimensional and three-dimensional imaging datasets from light, X-ray, and electron microscopy.

Figure 1:. Image segmentation workflow with CDeep3M.

In Steps 1-2 a new trained model is generated, based on training images and labels. For 3D segmentation tasks CDeep3M trains three different models seeing 1 frame (1fm), seeing 3 frames (3fm) and seeing 5 frames (5fm) that are applied in Step 3 to provide three predictions. Those are merged into a single ensemble model at the post-processing step (Step 3).

Figure 2:. Multimodal image segmentation using CDeep3M.

(a) Segmentation of nuclei in XRM volume of a 50μm mouse brain slice containing the hippocampal DG area used for cell counting and establishing a cell density profile across x-y-z. (b) Segmentation of cell type specific DNA profile allows identification of Purkinje cells. Overlay of 3D surface mesh of nuclei on light microscopic image of DAPI-stained mouse cerebellar brain section. Scale bar: 20μm. (c) Segmentation of vesicles and membranes on multi-tilt electron tomography of high-pressure frozen mouse brain section. Scale bar: 200nm. (d) Upper row: SBEM micrograph (left) Scale bar: 1μm, segmentation using pre-trained model before (middle) and after domain adaption (right). Lower row: segmentation of membranes, mitochondria and nuclei overlaid on SBEM data

Figure 3:. Synaptic vesicle counts on SBEM data using CDeep3M.

(a) SBEM volume acquired at 2.4nm × 24nm voxelsize (16000×10000×400 voxel). Performance tests were done on two terminals (Terminal 1: 3183 vesicles and Terminal 2: 1000 vesicles) comparing to several independent human counts. Scale: 5μm (b) Three consecutive sections of Terminal 1 in overview (left panels, Scale: 200nm) alongside the CDeep3M predictions and zoomed in (right panels, Scale: 40nm) show comparisons to human counts. Centroids of 3D objects occurring across sections are marked on most prevalent plane.