Assessment of histopathological data is not only difficult due to its varying appearance, e.g. caused by staining artifacts, but also due to its sheer size: Common whole slice images feature a resolution of 6000x4000 pixels. Therefore, finding rare events in such data sets is a challenging and tedious task and developing sophisticated computerized tools is not easy, especially when no or little training data is available. In this work, we propose learning-free yet effective approach based on context sensitive patch-histograms in order to find extramedullary hematopoiesis events in Hematoxylin-Eosin-stained images. When combined with a simple nucleus detector, one can achieve performance levels in terms of sensitivity 0.7146, specificity 0.8476 and accuracy 0.8353 which are very well comparable to a recently published approach based on random forests.
A methodology to study the relationship between clinical variables [e.g., prostate specific antigen (PSA) or Gleason score] and cancer spatial distribution is described. Three-dimensional (3-D) models of 216 glands are reconstructed from digital images of whole mount histopathological slices. The models are deformed into one prostate model selected as an atlas using a combination of rigid, affine, and B-spline deformable registration techniques. Spatial cancer distribution is assessed by counting the number of tumor occurrences among all glands in a given position of the 3-D registered atlas. Finally, a difference between proportions is used to compare different spatial distributions. As a proof of concept, we compare spatial distributions from patients with PSA greater and less than 5 ng/ml and from patients older and younger than 60 years. Results suggest that prostate cancer has a significant difference in the right zone of the prostate between populations with PSA greater and less than 5 ng/ml. Age does not have any impact in the spatial distribution of the disease. The proposed methodology can help to comprehend prostate cancer by understanding its spatial distribution and how it changes according to clinical parameters. Finally, this methodology can be easily adapted to other organs and pathologies.
Understanding the spatial distribution of prostate cancer and how it changes according to prostate specific antigen (PSA) values, Gleason score, and other clinical parameters may help comprehend the disease and increase the overall success rate of biopsies. This work aims to build 3D spatial distributions of prostate cancer and examine the extent and location of cancer as a function of independent clinical parameters. The border of the gland and cancerous regions from wholemount histopathological images are used to reconstruct 3D models showing the localization of tumor. This process utilizes color segmentation and interpolation based on mathematical morphological distance. 58 glands are deformed into one prostate atlas using a combination of rigid, affine, and b-spline deformable registration techniques. Spatial distribution is developed by counting the number of occurrences in a given position in 3D space from each registered prostate cancer. Finally a difference between proportions is used to compare different spatial distributions. Results show that prostate cancer has a significant difference (SD) in the right zone of the prostate between populations with PSA greater and less than 5ng/ml. Age does not have any impact in the spatial distribution of the disease. Positive and negative capsule-penetrated cases show a SD in the right posterior zone. There is SD in almost all the glands between cases with tumors larger and smaller than 10% of the whole prostate. A larger database is needed to improve the statistical validity of the test. Finally, information from whole-mount histopathological images may provide better insight into prostate cancer.
We developed a protocol for the acquisition of digital images and an algorithm for a color-based automatic segmentation
of cutaneous lesions of Leishmaniasis. The protocol for image acquisition provides control over the working
environment to manipulate brightness, lighting and undesirable shadows on the injury using indirect lighting. Also, this
protocol was used to accurately calculate the area of the lesion expressed in mm2 even in curved surfaces by combining
the information from two consecutive images. Different color spaces were analyzed and compared using ROC curves in
order to determine the color layer with the highest contrast between the background and the wound. The proposed
algorithm is composed of three stages: (1) Location of the wound determined by threshold and mathematical morphology
techniques to the H layer of the HSV color space, (2) Determination of the boundaries of the wound by analyzing the
color characteristics in the YIQ space based on masks (for the wound and the background) estimated from the first stage,
and (3) Refinement of the calculations obtained on the previous stages by using the discrete dynamic contours algorithm.
The segmented regions obtained with the algorithm were compared with manual segmentations made by a medical
specialist. Broadly speaking, our results support that color provides useful information during segmentation and
measurement of wounds of cutaneous Leishmaniasis. Results from ten images showed 99% specificity, 89% sensitivity,
and 98% accuracy.
This paper presents a semi-automated algorithm for prostate boundary segmentation from three-dimensional (3D)
ultrasound (US) images. The US volume is sampled into 72 slices which go through the center of the prostate gland and
are separated at a uniform angular spacing of 2.5 degrees. The approach requires the user to select four points from slices
(at 0, 45, 90 and 135 degrees) which are used to initialize a discrete dynamic contour (DDC) algorithm. 4 Support Vector
Machines (SVMs) are trained over the output of the DDC and classify the rest of the slices. The output of the SVMs is
refined using binary morphological operations and DDC to produce the final result. The algorithm was tested on seven
ex vivo 3D US images of prostate glands embedded in an agar mold. Results show good agreement with manual
segmentation.
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