Accurate detection of dysmorphic nuclei using dynamic programming and supervised classification

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows

Unsupervised classification of automatically segmented nuclei.

<p>(A) Overview of the morpho-textural feature set that was extracted from 324 segmented nuclei; (B) Heatmap representing the grayscale-coded z-scores of all the features (columns) for all individual nuclei (rows). Hierarchical clustering on this dataset largely, but not completely, separates normal (blue) from dysmorphic (red) nuclei populations as indicated by the dendrogram on the left. (C) Example nuclei that have been correctly or incorrectly clustered. Colored outlines represent the manually assigned class, whereas the colored bar represents the assigned class by clustering (blue: normal and red: dysmorphic nuclei). Numbers link segmentations of selected nuclei to their position in the heatmap; (D) Principal component analysis of the data set yields two distinct but not fully separated clusters for the two classes as illustrated by a bi-plot explaining 42% of the variance. Discrimination of the two groups is predominantly in the direction of PC1; (E) The factor map reveals correlated features in PC space.</p

Schematic representation of the error metrics used for validation of the segmentation algorithm.

<p>Individual BleND segmentations (red line) were compared to the respective GTs (green line) using two error metrics: the average Hausdorff distance (AHD) and a non-similarity index (NSI). The AHD is calculated as the average of the minimal distances (yellow arrows)–selected among all possible distances (examples in dotted grey arrows)–between the pixels of both contours. The NSI is derived as the non-overlapping area (red and green area) divided by the sum of the total area described by these contours (red and green line).</p

Performance errors of automatic segmentation methods.

<p>Performance errors of automatic segmentation methods.</p

Segmentation results for different cell types and contour refinement algorithms.

<p>(A) Segmentation results for DAPI counterstained nuclei of HDF-NCP (widefield microscopy), HDF-HGPS (widefield microscopy), HDF-NULL (widefield microscopy), HeLa-ZKO (point scanning confocal microscopy), HT-LKO (widefield microscopy) cells and mouse primary hippocampal neurons (spinning disk confocal microscopy). Blebbed (red arrowheads) and/or severely deformed nuclei (blue arrowheads) are accurately delineated; (B) Comparison of BleND with level set active contour and gradient vector flow active contour algorithms on an image of HDF-NCP cells. Insets show contrast-stretched, magnified views of selected regions. Due to locally weaker signals, blebs (red arrowheads) are poorly detected with the level set active contour algorithm. The gradient vector flow algorithm performs better, but fails to detect subtler blebs (region 1) and does not accurately delineate deep crevices surrounding blebs (regions 2,3).</p

Overview of the BleND segmentation algorithm.

<p>(A) Workflow of the algorithm built on (B) intensity-based segmentation, (C) contour refinement, and (D) conditional watershed; (B) The segmentation process is implemented as a two-pass thresholding algorithm that generates “initial ROIs” of nuclei in the preprocessed image (i: dysmorphic nucleus, ii: two juxtaposed normal nuclei). A global thresholding is performed on the image, which creates a binary mask (1). The objects identified herein are dilated by 3 μm and combined (Boolean AND operation) with a Voronoi tessellation mask to ascertain that the dilated objects do not fuse. For each resulting “seed ROI” (4), a local threshold (5) is determined yielding an initial nuclear ROI (6) that is more accurate than the seed ROI (note the improved segmentation for the dysmorphic and juxtaposed nuclei); (C) In the subsequent contour refinement procedure, the initial ROI is used (6) to straighten a 2μm wide region along the nuclear periphery (white dot indicates the point where the contour was opened and the white arrow indicates the direction of the straightening) (7). In this rectangular representation, the edge of the nucleus is enhanced by convolution with a vertical Sobel kernel (8). Then, an optimal path finding (OPF) algorithm determines the path with the highest path strength (9). The OPF algorithm effectively detects crevices surrounding nuclear blebs (red arrowhead). The contour of the nucleus is then reconstructed to generate a “refined ROI” and this process is repeated until the optimal path no longer changes (10); (D) To segment neighboring nuclei that could not be separated in the previous steps, a conditional watershed was implemented in which correct and incorrect splits were discriminated based on a size criterion and an intensity drop along the separation line (red arrowhead). This intensity drop is calculated as a median intensity profile perpendicular to the separation line (13). The user defines a threshold for the acquired intensity drop. In this example, the threshold is set at 0.75. If there is an intensity drop in the median profile of less than 25%—Min/Max intensity ratio above the 75% (dotted red) line (14)—the split is regarded as incorrect and the two parts of the nucleus are merged (15). If the drop is bigger, the split is regarded as being correct and it is retained to generate new nuclear ROIs.</p

Discrimination of dysmorphic nuclei based on elliptic Fourier descriptors.

<p>(A) Morphology-based ranking of HDF-NCP nuclei. Both dysmorphic (red) and normal (blue) nuclei of HDF-NCP are ranked according to their summed EFD value (color coded). Severely deformed nuclei have higher EFD values than nuclei with small blebs, which in turn have larger EFD values than regular, ovoid-shaped nuclei. (B) Texture-based ranking for HDF-NULL cells. Dysmorphic nuclei are characterized by an intensity gradient due to an chromatin ruffling. Normal and aberrant nuclei of comparable shape (EFD value in italic and in brackets) can be distinguished based on the value of the entropy texture parameter (color coded).</p

Comparison of performance errors for dysmorphic nuclei.

<p>Boxplots and dot plots of the performance errors of all threshold combinations using 1-pass (only global) or 2-pass thresholding (global and local), with (+CR) or without (-CR) contour refinement for (A) all nuclei, and (B) for dysmorphic nuclei only. Asterisks mark statistically significant differences according to the Wilcoxon rank-sum test (one sided) (* P < 0.05, *** P < 0.005). The outliers in the boxplots represent inadequate segmentations caused by an error-prone thresholding method. The color in the dot plots represents the error, with values falling within the error range of the ground truth (GT) comparisons displayed in green hues, and values exceeding this range in light grey. The numbers on the axes of the dot plots represent different threshold methods: 1: Huang, 2: Intermodes, 3: IJ_Isodata, 4: Isodata, 5: Li, 6: Maximum entropy, 7: Mean, 8: Minimum error, 9: Minimum, 10: Moment preserving, 11: Otsu, 12: Percentile, 13: RenyiEntropy, 14: Shanbhag, 15: Triangle, 16: Yen.</p