Accurate segmentation of overlapping nuclei is essential in determining nuclei count and evaluating the sub-cellular
localization of protein biomarkers in image Cytometry and Histology. Current cellular segmentation algorithms generally
lack fast and reliable methods for disambiguating clumped nuclei. In immuno-fluorescence segmentation, solutions to
challenges including nuclei misclassification, irregular boundaries, and under-segmentation require reliable separation of
clumped nuclei. This paper presents a fast and accurate algorithm for joint segmentation of cellular cytoplasm and nuclei
incorporating procedures for reliably separating overlapping nuclei. The algorithm utilizes a combination of ideas and is
a significant improvement on state-of-the-art algorithms for this application. First, an adaptive process that includes top-hat
filtering, blob detection and distance transforms estimates the inverse illumination field and corrects for intensity
non-uniformity. Minimum-error-thresholding based binarization augmented by statistical stability estimation is applied
prior to seed-detection constrained by a distance-map-based scale-selection to identify candidate seeds for nuclei
segmentation. The nuclei clustering step also incorporates error estimation based on statistical stability. This enables the
algorithm to perform localized error correction. Final steps include artifact removal and reclassification of nuclei objects
near the cytoplasm boundary as epithelial or stroma. Evaluation using 48 realistic phantom images with known ground-truth
shows overall segmentation accuracy exceeding 96%. It significantly outperformed two state-of-the-art algorithms
in clumped nuclei separation. Tests on 926 prostate biopsy images (326 patients) show that the segmentation
improvement improves the predictive power of nuclei architecture features based on the minimum spanning tree
algorithm. The algorithm has been deployed in a large scale pathology application.
Tissue segmentation is one of the key preliminary steps in the morphometric analysis of tissue architecture. In multi-channel
immunoflurorescent biomarker images, the primary segmentation steps consist of segmenting the nuclei
(epithelial and stromal) and epithelial cytoplasm from 4',6-diamidino-2-phenylindole (DAPI) and cytokeratin 18 (CK18)
biomarker images respectively. The epithelial cytoplasm segmentation can be very challenging due to variability in
cytoplasm morphology and image staining. A robust and adaptive segmentation algorithm was developed for the
purpose of both delineating the boundaries and separating thin gaps that separate the epithelial unit structures. This
paper discusses novel methods that were developed for adaptive segmentation of epithelial cytoplasm and separation of
epithelial units. The adaptive segmentation was performed by computing the non-epithelial background texture of every
CK18 biomarker image. The epithelial unit separation was performed using two complementary techniques: a marker
based, center-initialized watershed transform and a boundary initialized fast marching-watershed segmentation. The
adaptive segmentation algorithm was tested on 926 CK18 biomarker biopsy images (326 patients) with limited
background noise and 1030 prostatectomy images (374 patients) with noisy to very noisy background. The segmentation
performance was measured using two different methods, namely; stability and background textural metrics. It was
observed that the database of 1030 noisy prostatectomy images had a lower mean value (using stability and three
background texture performance metrics) compared to the biopsy dataset of 926 images that had limited background
noise. The average of all four performance metrics yielded 94.32% accuracy for prostatectomy images compared to
99.40% accuracy for biopsy images.
Performance assessment of segmentation algorithms compares segmentation outputs to a handful of manually obtained
ground-truth. This assumes that the ground-truth images are accurate, reliable and representative of the entire image set.
In image cytometry, few ground-truth images are typically used because of the difficulty of manually segmenting images
with large numbers of small objects. This violates the aforementioned assumptions. Automated methods of segmentation
evaluation without ground-truth are needed. We describe a stable and reliable method for evaluating segmentation
performance without ground-truth. Segmentation errors are either statistical or structural. Statistical errors reflect failure
to account for random variations in pixel values while structural errors result from inadequate image description models.
As statistical errors predominate image cytometry, our method focuses on statistical stability assessment. For any image-algorithm
pair, we obtain multiple perturbed variants of the image by applying slight linear blur. We segment the image
and its variants with the algorithm and determine the match between the output from the image and the output from its
variants. We utilized 48 realistic phantom images with known ground-truth and four segmentation algorithms with large
performance differences to assess the efficacy of the method. For each algorithm-image pair, we obtained a ground truth
match score and four different statistical validation scores. Analyses show that statistical validation and ground-truth
validation scores correlate in over 96% of cases. The statistical validation approach reduces segmentation review time
and effort by over 99% and enables assessment of segmentation quality long after an algorithm has been deployed.
Automatic segmentation of cellular structures is an essential step in image cytology and histology. Despite substantial
progress, better automation and improvements in accuracy and adaptability to novel applications are needed. In
applications utilizing multi-channel immuno-fluorescence images, challenges include misclassification of epithelial and
stromal nuclei, irregular nuclei and cytoplasm boundaries, and over and under-segmentation of clustered nuclei.
Variations in image acquisition conditions and artifacts from nuclei and cytoplasm images often confound existing
algorithms in practice. In this paper, we present a robust and accurate algorithm for jointly segmenting cell nuclei and
cytoplasm using a combination of ideas to reduce the aforementioned problems. First, an adaptive process that includes
top-hat filtering, Eigenvalues-of-Hessian blob detection and distance transforms is used to estimate the inverse
illumination field and correct for intensity non-uniformity in the nuclei channel. Next, a minimum-error-thresholding
based binarization process and seed-detection combining Laplacian-of-Gaussian filtering constrained by a distance-map-based
scale selection is used to identify candidate seeds for nuclei segmentation. The initial segmentation using a local
maximum clustering algorithm is refined using a minimum-error-thresholding technique. Final refinements include an
artifact removal process specifically targeted at lumens and other problematic structures and a systemic decision process
to reclassify nuclei objects near the cytoplasm boundary as epithelial or stromal. Segmentation results were evaluated
using 48 realistic phantom images with known ground-truth. The overall segmentation accuracy exceeds 94%. The
algorithm was further tested on 981 images of actual prostate cancer tissue. The artifact removal process worked in 90%
of cases. The algorithm has now been deployed in a high-volume histology analysis application.
Scoliosis affects the alignment of the spine and the shape of the torso. Most scoliosis patients and their families are more concerned about the effect of scoliosis on the torso than its effect on the spine. There is a need to develop robust techniques for quantifying torso deformity based on full torso scans. In this paper, deformation indices obtained from orthogonal maps of full torso scans are used to quantify torso deformity in scoliosis. 'Orthogonal maps' are obtained by applying orthogonal transforms to 3D surface maps. (An 'orthogonal transform' maps a cylindrical coordinate system to a Cartesian coordinate system.) The technique was tested on 361 deformed computer models of the human torso and on 22 scans of volunteers (8 normal and 14 scoliosis). Deformation indices from the orthogonal maps correctly classified up to 95% of the volunteers with a specificity of 1.00 and a sensitivity of 0.91. In addition to classifying scoliosis, the system gives a visual representation of the entire torso in one view and is viable for use in a clinical environment for managing scoliosis.
The influence of posture and re-positioning (sway and breathing) on the accuracy of a torso imaging system for assessing scoliosis was evaluated. The system comprised of a rotating positioning platform and one or two laser digitizers. It required four partial-scans taken at 90<sup>o</sup> intervals over 10 seconds to generate two complete torso scans. Its accuracy was previously determined to be 1.1±0.9mm. Ten evenly spaced cross-sections obtained from forty scans of five volunteers in four postures (free-standing, holding side supports, holding front supports and with their hands on their shoulders) were used to assess the variability due to posture. Twenty cross-sections from twenty scans of two volunteers holding side supports were used to assess the variability due to positioning. The variability due to posture was less than 4mm at each cross-section for all volunteers. Variability due to sway ranged from 0-3.5mm while that due to breathing ranged from 0-3mm for both volunteers. Holding side supports was the best posture. Taking the four shots within 10 seconds was optimal. As major torso features that are indicative of scoliosis are larger than 4mm in size, the system could be used in obtaining complete torso images used in assessing and managing scoliosis.