Segmentation of Multi-Isotope Imaging Mass Spectrometry Data for Semi-Automatic Detection of Regions of Interest

Multi-isotope imaging mass spectrometry (MIMS) associates secondary ion mass spectrometry (SIMS) with detection of several atomic masses, the use of stable isotopes as labels, and affiliated quantitative image-analysis software. By associating image and measure, MIMS allows one to obtain quantitative information about biological processes in sub-cellular domains. MIMS can be applied to a wide range of biomedical problems, in particular metabolism and cell fate [1], [2], [3]. In order to obtain morphologically pertinent data from MIMS images, we have to define regions of interest (ROIs). ROIs are drawn by hand, a tedious and time-consuming process. We have developed and successfully applied a support vector machine (SVM) for segmentation of MIMS images that allows fast, semi-automatic boundary detection of regions of interests. Using the SVM, high-quality ROIs (as compared to an expert's manual delineation) were obtained for 2 types of images derived from unrelated data sets. This automation simplifies, accelerates and improves the post-processing analysis of MIMS images. This approach has been integrated into “Open MIMS,” an ImageJ-plugin for comprehensive analysis of MIMS images that is available online at http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php

MIMS images of mouse cochlea tissue sections.

<p>Quantitative images based on measured masses of ¹²C¹⁴N⁻ (A) and ¹²C¹⁵N⁻ (B), representing the detected amount of the respective nitrogen isotopes at each pixel. Dividing the values at each pixel results in a ratio image (C), which determines the isotopic ratio of nitrogen at each position within the section. (D) is the ¹²C¹⁵N/¹²C¹⁴N HSI of the data. Scale bars on (C) and (D) range from the natural ratio to the value in the ¹⁵N-enriched chow, which corresponds to the maximum ratio that could be reached in newly synthesized protein (multiplied by 10000). Field 41×41 µm, 512×512 pixels, acquisition time 240 minutes.</p

P-values of the homogeneity statistic h_c for each class of the mouse brain image (Figure 2E).

<p>P-values were derived from a null-distribution by repeated random-assignments of ROIs. Nominal p-values of 0 occur if not a single random-sample achieved a better statistic than the observed one. Statistics for all classes, except for class 4, are significant at the 5% level. N = 10000.</p

HSI images, Expert ROIs, and Segmentation Results.

<p>(A) is the ¹²C¹⁵N/¹²C¹⁴N HSI image of a mouse cochlea section with a field of 41×41 µm, 512×512 pixels, and acquisition time of 240 minutes. Scale bars range from the natural ratio to the value in the slightly ¹⁵N-enriched chow, which corresponds to the maximum ratio that could be reached in newly synthesized protein (multiplied by 10000). (B) is a complete segmentation of (A). (C) and (D) are insets of (A) and (B), respectively. (C) shows expert freehand-drawn ROIs (white borders) used for training the SVM. (D) is the complete segmentation of (C). We chose 6 classes of ratio value ensembles guided by the HSI image and represented in (B) and (D) by 6 hues spanning the rainbow colors from blue (lowest ratio) to red (highest). The SVM used the ¹²C¹⁴N quantitative MIMS image and the derived ¹²C¹⁵N/¹²C¹⁴N ratio image. (E) is the ¹²C¹⁵N/¹²C¹⁴N HSI image of a mouse brain section, field 50×50 µm, 256×256 pixels, acquisition time of 11 minutes. Scale bars range from the natural ratio to the maximum value measured in the brain after feeding with a maximally 15N-enriched chow (∼98%). (F) is a complete segmentation of (E). (G) and (H) are insets of (E) and (F), respectively. (G) shows expert freehand-drawn ROIs (white borders) used for training the SVM. We chose 6 classes of ratio value ensembles guided by the HSI image and represented in (F) and (H) by 6 hues spanning the rainbow colors from blue (lowest ratio) to red (highest). The SVM used only the derived ¹²C¹⁵N/¹²C¹⁴N ratio image. ROI boundaries (which have zero width) in (C) and (G) have been thickened to 1 pixel for clarity.</p

Segmentation of higher channel data.

<p>A MIMS image of a mouse intestinal crypt <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030576#pone.0030576-Steinhauser1" target="_blank">[23]</a> showing ¹⁵N-thymidine labeled nuclei (solid arrows), unlabeled nuclei (double arrows) and sulfur-containing granules (outlined arrow). The images are ¹²C¹⁴N (A), ³¹P (B), ³²S (C), ¹²C¹⁵N/¹²C¹⁴N (D), ¹²C¹⁵N/¹²C¹⁴N HSI (E). Scale bars in (D) and (E) range from the natural ratio to a value that clearly delineates the borders of labeled nuclei (times 10000). The resulting segmentation is shown in (F). Field 30×30 µm, 512×512 pixels, acquisition time 849 minutes.</p

Cross-validation results of performance analysis.

<p>Violin plots showing the probability density functions of classification performance on MIMS images of the cochlea (A) and brain (B), <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030576#pone-0030576-g002" target="_blank">Figures 2A and 2E</a>, respectively. For each class, recall (blue) and precision (red) were calculated by cross-validation on 20% of the expert-annotated ROIs. N = 500 in both cases.</p

Cross-segmentation performance.

<p>Recall and precision values for each class of the cross-segmentation of the brain image in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030576#pone-0030576-g002" target="_blank">Figure 2E</a> using a model trained an image from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030576#pone-0030576-g006" target="_blank">Figure 6</a> (bottom left image, double arrow). (A) The predictive performance of the cross-segmentation was evaluated on the expert-annotated data. (B) Comparison of direct- versus cross-segmentation based on the complete image segmentation.</p

Cross-segmentation of consecutive brain sections.

<p>Example of segmentation of a series of images with the ¹²C¹⁵N/¹²C¹⁴N HSI shown in the left column and the full segmentation result shown in the right column; six fields total, 8 expert defined classes. The image from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030576#pone-0030576-g002" target="_blank">Figure 2E</a> is at the top left (arrow). SVM trained on bottom left image (double arrow). These images were part of an acquisition of 37 images, 50×50 µm, 256×256 pixels, acquisition time 11 minutes.</p

Segmentation of Volumes.

<p>MIMS image of mouse stereocilia <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030576#pone.0030576-Zhang2" target="_blank">[24]</a> from an 8×8×2-µm volume in 256×256×90 voxels (acquisition time 1966 minutes). Renderings of ¹²C¹⁴N (A), ¹²C¹⁵N/¹²C¹⁴N HSI (B) both rendered in ImageVis3D <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030576#pone.0030576-ImageVis3D1" target="_blank">[25]</a>. The scale bar in (B) ranges from the natural ratio to the value in the ¹⁵N-enriched chow, which corresponds to the maximum ratio that could be reached in newly synthesized protein (multiplied by 10000). Regions of high turnover (solid arrow), medium turnover (double arrow), and low turnover (outlined arrow) are clearly visible in (B). The resulting segmentation (C) is rendered in Seg3D <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030576#pone.0030576-Seg3D1" target="_blank">[26]</a> and these same classes of high, medium, and low turnover are shown colored as red, green, and blue respectively.</p

Cross-validation results of robustness analysis.

<p>Violin plots describing the robustness evaluated on the MIMS images of the cochlea (A) and brain (B), <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030576#pone-0030576-g002" target="_blank">Figures 2A and 2E</a>, respectively. For each class, recall was calculated by comparing the reference prediction (using all training data) with predictions using 50 (red), 25 (blue), 10 (yellow) or 5 (green) percent of randomly sampled training data. N = 500 in all cases.</p