Glandular Segmentation of Prostate Cancer: An Illustration of How the Choice of Histopathological Stain Is One Key to Success for Computational Pathology.

Digital pathology offers the potential for computer-aided diagnosis, significantly reducing the pathologists' workload and paving the way for accurate prognostication with reduced inter-and intra-observer variations. But successful computer-based analysis requires careful tissue preparation and image acquisition to keep color and intensity variations to a minimum. While the human eye may recognize prostate glands with significant color and intensity variations, a computer algorithm may fail under such conditions. Since malignancy grading of prostate tissue according to Gleason or to the International Society of Urological Pathology (ISUP) grading system is based on architectural growth patterns of prostatic carcinoma, automatic methods must rely on accurate identification of the prostate glands. But due to poor color differentiation between stroma and epithelium from the common stain hematoxylin-eosin, no method is yet able to segment all types of glands, making automatic prognostication hard to attain. We address the effect of tissue preparation on glandular segmentation with an alternative stain, Picrosirius red-hematoxylin, which clearly delineates the stromal boundaries, and couple this stain with a color decomposition that removes intensity variation. In this paper we propose a segmentation algorithm that uses image analysis techniques based on mathematical morphology and that can successfully determine the glandular boundaries. Accurate determination of the stromal and glandular morphology enables the identification of the architectural pattern that determine the malignancy grade and classify each gland into its appropriate Gleason grade or ISUP Grade Group. Segmentation of prostate tissue with the new stain and decomposition method has been successfully tested on more than 11000 objects including well-formed glands (Gleason grade 3), cribriform and fine caliber glands (grade 4), and single cells (grade 5) glands.

Comparison of Walking and Traveling-Wave Piezoelectric Motors as Actuators in Kinesthetic Haptic Devices.

Piezoelectric motors offer an attractive alternative to electromagnetic actuators in portable haptic interfaces: they are compact, have a high force-to-volume ratio, and can operate with limited or no gearing. However, the choice of a piezoelectric motor type is not obvious due to differences in performance characteristics. We present our evaluation of two commercial, operationally different, piezoelectric motors acting as actuators in two kinesthetic haptic grippers, a walking quasi-static motor and a traveling wave ultrasonic motor. We evaluate each gripper's ability to display common virtual objects including springs, dampers, and rigid walls, and conclude that the walking quasi-static motor is superior at low velocities. However, for applications where high velocity is required, traveling wave ultrasonic motors are a better option.

Haptics-assisted Virtual Planning of Bone, Soft Tissue, and Vessels in Fibula Osteocutaneous Free Flaps.

UNLABELLED: Virtual surgery planning has proven useful for reconstructing head and neck defects by fibula osteocutaneous free flaps (FOFF). Benefits include improved healing, function, and aesthetics, as well as cost savings. But available virtual surgery planning systems incorporating fibula in craniomaxillofacial reconstruction simulate only bone reconstruction without considering vessels and soft tissue. METHODS: The Haptics-Assisted Surgery Planning (HASP) system incorporates bone, vessels, and soft tissue of the FOFF in craniomaxillofacial defect reconstruction. Two surgeons tested HASP on 4 cases they had previously operated on: 3 with composite mandibular defects and 1 with a composite cervical spine defect. With the HASP stereographics and haptic feedback, using patient-specific computed tomography angiogram data, the surgeons planned the 4 cases, including bone resection, fibula design, recipient vessels selection, pedicle and perforator location selection, and skin paddle configuration. RESULTS: Some problems encountered during the actual surgery could have been avoided as they became evident with HASP. In one case, the fibula reconstruction was incomplete because the fibula had to be reversed and thus did not reach the temporal fossa. In another case, the fibula had to be rotated 180 degrees to correct the plate and screw placement in relation to the perforator. In the spinal case, difficulty in finding the optimal fibula shape and position required extra ischemia time. CONCLUSIONS: The surgeons found HASP to be an efficient planning tool for FOFF reconstructions. The testing of alternative reconstructions to arrive at an optimal FOFF solution preoperatively potentially improves patient function and aesthetics and reduces operating room time.

Histological stain evaluation for machine learning applications.

AIMS: A methodology for quantitative comparison of histological stains based on their classification and clustering performance, which may facilitate the choice of histological stains for automatic pattern and image analysis. BACKGROUND: Machine learning and image analysis are becoming increasingly important in pathology applications for automatic analysis of histological tissue samples. Pathologists rely on multiple, contrasting stains to analyze tissue samples, but histological stains are developed for visual analysis and are not always ideal for automatic analysis. MATERIALS AND METHODS: Thirteen different histological stains were used to stain adjacent prostate tissue sections from radical prostatectomies. We evaluate the stains for both supervised and unsupervised classification of stain/tissue combinations. For supervised classification we measure the error rate of nonlinear support vector machines, and for unsupervised classification we use the Rand index and the F-measure to assess the clustering results of a Gaussian mixture model based on expectation-maximization. Finally, we investigate class separability measures based on scatter criteria. RESULTS: A methodology for quantitative evaluation of histological stains in terms of their classification and clustering efficacy that aims at improving segmentation and color decomposition. We demonstrate that for a specific tissue type, certain stains perform consistently better than others according to objective error criteria. CONCLUSIONS: The choice of histological stain for automatic analysis must be based on its classification and clustering performance, which are indicators of the performance of automatic segmentation of tissue into morphological components, which in turn may be the basis for diagnosis.

A haptics-assisted cranio-maxillofacial surgery planning system for restoring skeletal anatomy in complex trauma cases.

PURPOSE:    Cranio-maxillofacial (CMF) surgery to restore normal skeletal anatomy in patients with serious trauma to the face can be both complex and time-consuming. But it is generally accepted that careful pre-operative planning leads to a better outcome with a higher degree of function and reduced morbidity in addition to reduced time in the operating room. However, today's surgery planning systems are primitive, relying mostly on the user's ability to plan complex tasks with a two-dimensional graphical interface. METHODS:    A system for planning the restoration of skeletal anatomy in facial trauma patients using a virtual model derived from patient-specific CT data. The system combines stereo visualization with six degrees-of-freedom, high-fidelity haptic feedback that enables analysis, planning, and preoperative testing of alternative solutions for restoring bone fragments to their proper positions. The stereo display provides accurate visual spatial perception, and the haptics system provides intuitive haptic feedback when bone fragments are in contact as well as six degrees-of-freedom attraction forces for precise bone fragment alignment. RESULTS:    A senior surgeon without prior experience of the system received 45 min of system training. Following the training session, he completed a virtual reconstruction in 22 min of a complex mandibular fracture with an adequately reduced result. CONCLUSION:    Preliminary testing with one surgeon indicates that our surgery planning system, which combines stereo visualization with sophisticated haptics, has the potential to become a powerful tool for CMF surgery planning. With little training, it allows a surgeon to complete a complex plan in a short amount of time.

Blind color decomposition of histological images.

Cancer diagnosis is based on visual examination under a microscope of tissue sections from biopsies. But whereas pathologists rely on tissue stains to identify morphological features, automated tissue recognition using color is fraught with problems that stem from image intensity variations due to variations in tissue preparation, variations in spectral signatures of the stained tissue, spectral overlap and spatial aliasing in acquisition, and noise at image acquisition. We present a blind method for color decomposition of histological images. The method decouples intensity from color information and bases the decomposition only on the tissue absorption characteristics of each stain. By modeling the charge-coupled device sensor noise, we improve the method accuracy. We extend current linear decomposition methods to include stained tissues where one spectral signature cannot be separated from all combinations of the other tissues' spectral signatures. We demonstrate both qualitatively and quantitatively that our method results in more accurate decompositions than methods based on non-negative matrix factorization and independent component analysis. The result is one density map for each stained tissue type that classifies portions of pixels into the correct stained tissue allowing accurate identification of morphological features that may be linked to cancer.

Microarray core detection by geometric restoration.

Whole-slide imaging of tissue microarrays (TMAs) holds the promise of automated image analysis of a large number of histopathological samples from a single slide. This demands high-throughput image processing to enable analysis of these tissue samples for diagnosis of cancer and other conditions. In this paper, we present a completely automated method for the accurate detection and localization of tissue cores that is based on geometric restoration of the core shapes without placing any assumptions on grid geometry. The method relies on hierarchical clustering in conjunction with the Davies-Bouldin index for cluster validation in order to estimate the number of cores in the image wherefrom we estimate the core radius and refine this estimate using morphological granulometry. The final stage of the algorithm reconstructs circular discs from core sections such that these discs cover the entire region of each core regardless of the precise shape of the core. The results show that the proposed method is able to reconstruct core locations without any evidence of localization. Furthermore, the algorithm is more efficient than existing methods based on the Hough transform for circle detection. The algorithm's simplicity, accuracy, and computational efficiency allow for automated high-throughput analysis of microarray images.