Automated and Accurate Detection of Soma Location and Surface Morphology in Large-Scale 3D Neuron Images

<div><p></p><p>Automated and accurate localization and morphometry of somas in 3D neuron images is essential for quantitative studies of neural networks in the brain. However, previous methods are limited in obtaining the location and surface morphology of somas with variable size and uneven staining in large-scale 3D neuron images. In this work, we proposed a method for automated soma locating in large-scale 3D neuron images that contain relatively sparse soma distributions. This method involves three steps: (i) deblocking the image with overlap between adjacent sub-stacks; (ii) locating the somas in each small sub-stack using multi-scale morphological close and adaptive thresholds; and (iii) fusion of the repeatedly located somas in all sub-stacks. We also describe a new method for the accurate detection of the surface morphology of somas containing hollowness; this was achieved by improving the classical Rayburst Sampling with a new gradient-based criteria. Three 3D neuron image stacks of different sizes were used to quantitatively validate our methods. For the soma localization algorithm, the average recall and precision were greater than 93% and 96%, respectively. For the soma surface detection algorithm, the overlap of the volumes created by automatic detection of soma surfaces and manually segmenting soma volumes was more than 84% for 89% of all correctly detected somas. Our method for locating somas can reveal the soma distributions in large-scale neural networks more efficiently. The method for soma surface detection will serve as a valuable tool for systematic studies of neuron types based on neuron structure.</p></div

The deblocking strategy used for soma detection in a large-scale image stack.

<p><b>(A)</b> The maximum intensity projection of the original image stack (thickness: 150 µm). <b>(B)</b> Enlarged view of the dashed box in <b>A</b>. (C) Schematic diagram of our deblocking strategy. For simplicity, we only show our deblocking strategy using the 2D version of our method. We drew numerous square grids on a 2D image, and the side length of the square grids was 30 µm. The side length for each sub-stack was 60 µm. The overlap between adjacent sub-stacks was 30 µm. Three adjacent sub-stacks (red, green, and blue dashed boxes) around the red soma were used.</p

Validation of the proposed method for soma surface detection using two image stacks.

<p>Validation of the proposed method for soma surface detection using two image stacks.</p

Validation of soma surface detection in image stack 1.

<p>(A) The image stack 1 including all extracted image sub-stacks. (B) The automatically detected soma volumes (green) were overlaid on the manually segmented soma volumes (magenta). The soma in the red circle was missed; thus, the volume overlap ratio was zero. The two closely spaced somas in the yellow circle were falsely detected as a single soma; thus, the automatically detected soma volume included both of these two somas. The sickle-like soma in the blue circle was too irregular to be detected accurately. (C) The volume overlap ratio for all 27 total somas.</p

Validation of proposed method for soma location using three image stacks.

<p>Validation of proposed method for soma location using three image stacks.</p

Algorithm for locating a soma in a local sub-stack.

<p>(A) The original image sub-stack. (B) The maximum intensity projection image of the sub-stack in (A) along three orthogonal orientations. (C) The images after Gaussian smoothing. (D) The images after grayscale morphological closing. (E) The binary volume intersection including the soma volume achieved by the backprojection of the three projection images into 3D image space. (F) The images including the soma centroid obtained by connected component analysis and computation of the center of mass.</p

The algorithm for soma surface detection.

<p>(A) The surface (green) of the binary volume intersection (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062579#pone-0062579-g004" target="_blank">Fig. 4 E</a>) obtained by the original Rayburst Sampling method was overlaid on the extracted image sub-stack. (B) The 3D gradient image shown in Amira in orthoslice view was computed from the image stack in (A). (C) Gradient-based Rayburst Sampling. A series of discrete rays (long green dashed line) was emitted from the soma centroid (red circle); the surface (red contour) of the volume intersection was obtained by the original Rayburst Sampling method. Then, a reverse search within a certain distance (short green dashed line) was conducted from each point on the surface along the reverse direction of ray casting to find a local gradient maximum (orange circle). Together, the positions of local gradient maxima made up the final detected soma surface. (D) The final detected soma surface (green) was overlaid on the extracted image sub-stack.</p

The main features of images acquired from a Golgi-stained whole mouse brain using MOST.

<p>(A) A half of a coronal slice used for soma size measurement. (B) The local 3D image stack including random soma distributions and comparatively dense neurites from (A). The three somas surrounded with yellow circles correspond to the three somas in (C). (C) Examples showing the hollowness of somas in (B). The first column shows the projection images of the sub-stacks containing the three somas. The right image sequence shows discontinuous sections with fixed interval (10 µm) in each sub-stack.</p

The complete procedure for the proposed method.

<p>The complete procedure for the proposed method.</p

The results of soma location and surface detection for image stack 1.

<p>(A) The located soma centroids (green spheres) for image stack 1 were overlaid on the extracted image stack. The red circle indicates a missing soma; the yellow circle indicates the falsely detected soma (two somas were detected as only one soma). (B) The detected soma surface (green) for image stack 1 was overlaid on the original image stack. The automatically detected soma surfaces are shown in transparent blue and overlaid on the extracted image stacks.</p