Multiplex brightfield immunohistochemistry (IHC) offers the potential advantage to simultaneously analyze multiple biomarkers in order to, for example, determine T-cell numbers and phenotypes in a patient’s immune response to cancer. This paper presents a fully automatic image-analysis framework to utilize multiplex assays to identify and count stained cells of interest; it was validated by comparison with multiple “gold standard” 3,3'-Diaminobenzidine (DAB) singleplex assays. Both multiplex and singleplex assays were digitized using an RGB slide scanner. The proposed image-analysis algorithms consist of 1) a novel color-deconvolution method, 2) cell candidate detection, 3) feature extraction, and 4) cell classification based on supervised machine learning. Fully automated cell counts on the singleplex images were first rigorously verified by comparing to experts’ ground truth counts: A total of 72,076 for CD3-, 34,133 for CD8-, and 2,615 for FoxP3-positive T-cells were used in this singleplex algorithm validation. Concordance correlation coefficients (CCC) of the singleplex algorithm-to-observer agreements were 0.945, 0.965, and 0.997, respectively. Then, the singleplex slides were registered to the adjacent multiplex slides and the automated cell counts for each were compared. For this validation of the multiplex assay cell counts, the CCC values were 0.914, 0.943, and 0.877 for 12,828, 2,545, and 1,647 cells, respectively; we observed good slide-to-slide agreement between multiplex and singleplex. We conclude that the proposed fully-automated image analysis can be a useful and reliable tool to assess multiplex IHC assays.
Multiplex-brightfield immunohistochemistry (IHC) staining and quantitative measurement of multiple biomarkers can
support therapeutic targeting of carcinoma-associated fibroblasts (CAF). This paper presents an automated digitalpathology
solution to simultaneously analyze multiple biomarker expressions within a single tissue section stained with
an IHC duplex assay. Our method was verified against ground truth provided by expert pathologists. In the first stage,
the automated method quantified epithelial-carcinoma cells expressing cytokeratin (CK) using robust nucleus detection
and supervised cell-by-cell classification algorithms with a combination of nucleus and contextual features. Using
fibroblast activation protein (FAP) as biomarker for CAFs, the algorithm was trained, based on ground truth obtained
from pathologists, to automatically identify tumor-associated stroma using a supervised-generation rule. The algorithm
reported distance to nearest neighbor in the populations of tumor cells and activated-stromal fibroblasts as a wholeslide
measure of spatial relationships. A total of 45 slides from six indications (breast, pancreatic, colorectal, lung, ovarian,
and head-and-neck cancers) were included for training and verification. CK-positive cells detected by the algorithm were
verified by a pathologist with good agreement (R2=0.98) to ground-truth count. For the area occupied by FAP-positive
cells, the inter-observer agreement between two sets of ground-truth measurements was R2=0.93 whereas the algorithm
reproduced the pathologists’ areas with R2=0.96. The proposed methodology enables automated image analysis to
measure spatial relationships of cells stained in an IHC-multiplex assay. Our proof-of-concept results show an automated
algorithm can be trained to reproduce the expert assessment and provide quantitative readouts that potentially support a
cutoff determination in hypothesis testing related to CAF-targeting-therapy decisions.
Spectral computed tomography (SCT) generates better image quality than conventional computed tomography (CT). It has overcome several limitations for imaging atherosclerotic plaque. However, the literature evaluating the performance of SCT based on objective image assessment is very limited for the task of discriminating plaques. We developed a numerical-observer method and used it to assess performance on discrimination vulnerable-plaque features and compared the performance among multienergy CT (MECT), dual-energy CT (DECT), and conventional CT methods. Our numerical observer was designed to incorporate all spectral information and comprised two-processing stages. First, each energy-window domain was preprocessed by a set of localized channelized Hotelling observers (CHO). In this step, the spectral image in each energy bin was decorrelated using localized prewhitening and matched filtering with a set of Laguerre–Gaussian channel functions. Second, the series of the intermediate scores computed from all the CHOs were integrated by a Hotelling observer with an additional prewhitening and matched filter. The overall signal-to-noise ratio (SNR) and the area under the receiver operating characteristic curve (AUC) were obtained, yielding an overall discrimination performance metric. The performance of our new observer was evaluated for the particular binary classification task of differentiating between alternative plaque characterizations in carotid arteries. A clinically realistic model of signal variability was also included in our simulation of the discrimination tasks. The inclusion of signal variation is a key to applying the proposed observer method to spectral CT data. Hence, the task-based approaches based on the signal-known-exactly/background-known-exactly (SKE/BKE) framework and the clinical-relevant signal-known-statistically/background-known-exactly (SKS/BKE) framework were applied for analytical computation of figures of merit (FOM). Simulated data of a carotid-atherosclerosis patient were used to validate our methods. We used an extended cardiac-torso anthropomorphic digital phantom and three simulated plaque types (i.e., calcified plaque, fatty-mixed plaque, and iodine-mixed blood). The images were reconstructed using a standard filtered backprojection (FBP) algorithm for all the acquisition methods and were applied to perform two different discrimination tasks of: (1) calcified plaque versus fatty-mixed plaque and (2) calcified plaque versus iodine-mixed blood. MECT outperformed DECT and conventional CT systems for all cases of the SKE/BKE and SKS/BKE tasks (all p<0.01). On average of signal variability, MECT yielded the SNR improvements over other acquisition methods in the range of 46.8% to 65.3% (all p<0.01) for FBP-Ramp images and 53.2% to 67.7% (all p<0.01) for FBP-Hanning images for both identification tasks. This proposed numerical observer combined with our signal variability framework is promising for assessing material characterization obtained through the additional energy-dependent attenuation information of SCT. These methods can be further extended to other clinical tasks such as kidney or urinary stone identification applications.
Dental implant is one of the most popular methods of tooth root replacement used in prosthetic dentistry. Computerize
navigation system on a pre-surgical plan is offered to minimize potential risk of damage to critical anatomic structures of
patients. Dental tool tip calibrating is basically an important procedure of intraoperative surgery to determine the relation
between the hand-piece tool tip and hand-piece's markers. With the transferring coordinates from preoperative CT data
to reality, this parameter is a part of components in typical registration problem. It is a part of navigation system which
will be developed for further integration. A high accuracy is required, and this relation is arranged by point-cloud-to-point-cloud rigid transformations and singular value decomposition (SVD) for minimizing rigid registration errors. In earlier studies, commercial surgical navigation systems from, such as, BrainLAB and Materialize, have flexibility problem on tool tip calibration. Their systems either require a special tool tip calibration device or are unable to change the different tool. The proposed procedure is to use the pointing device or hand-piece to touch on the pivot and the transformation matrix. This matrix is calculated every time when it moves to the new position while the tool tip stays at the same point. The experiment acquired on the information of tracking device, image acquisition and image processing algorithms. The key success is that point-to-point-cloud requires only 3 post images of tool to be able to converge to the minimum errors 0.77%, and the obtained result is correct in using the tool holder to track the path simulation line displayed in graphic animation.