Three-dimensional DNA image cytometry by optical projection tomographic microscopy for early cancer diagnosis

Determining the Indeterminate Biliary Stricture: Cholangioscopy and Beyond

PURPOSE OF REVIEW

Indeterminate biliary strictures (IDBS) continue to be an area of frustration for clinicians. Standard endoscopic retrograde cholangiopancreatography (ERCP) with conventional brush cytology and/or forceps biopsy has a low sensitivity for distinguishing benign from malignant biliary strictures. A delay in diagnosis of malignancy has consequences for subsequent therapy or surgery. In this article, we review current and emerging technologies that may aid in this diagnostic dilemma.

RECENT FINDINGS

Several technologies have been utilized in IDBS to establish a diagnosis which include peroral cholangioscopy, confocal laser endomicroscopy, endoscopic ultrasound with fine needle aspiration, intraductal ultrasound, optical coherence tomography, fluorescence in situ hybridization, next generation sequencing, integrated molecular pathology, and DNA-image cytometry. While cholangioscopy and confocal laser endomicroscopy have become standards of care in expert centers for the evaluation of patients with IDBS, there are several endoscopic and molecular modalities that may also aid in establishing a diagnosis. Further head-to-head prospective diagnostic studies as well as cost-efficacy studies are needed.

Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes

Measurement of nuclear-to-cytoplasm (N:C) ratios plays an important role in detection of atypical and tumor cells. Yet, current clinical methods rely heavily on immunofluroescent staining and manual reading. To achieve the goal of rapid and label-free cell classification, realistic optical cell models (OCMs) have been developed for simulation of diffraction imaging by single cells. A total of 1892 OCMs were obtained with varied nuclear volumes and orientations to calculate cross-polarized diffraction image (p-DI) pairs divided into three nuclear size groups of OCM_S , OCM_O and OCM_L based on three prostate cell structures. Binary classifications were conducted among the three groups with image parameters extracted by the algorithm of gray-level co-occurrence matrix. The averaged accuracy of support vector machine (SVM) classifier on test dataset of p-DI was found to be 98.8% and 97.5% respectively for binary classifications of OCM_S vs OCM_O and OCM_O vs OCM_L for the prostate cancer cell structure. The values remain about the same at 98.9% and 97.8% for the smaller prostate normal cell structures. The robust performance of SVM over clustering classifiers suggests that the high-order correlations of diffraction patterns are potentially useful for label-free detection of single cells with large N:C ratios.

Dual-modality optical projection tomography reconstruction method from fewer views

In optical projection tomography (OPT), for longitudinal living model studies, multiple measurements of the same sample are required at different time points. It is important to decrease both the total acquisition time and the light dose to the sample. We improved the ordered subsets expectation maximization reconstruction algorithm for OPT, which reduces the acquisition time and number of projections greatly compared with filtered back projection (FBP), and obtained satisfactory reconstructed images. Using zebrafish, in transmission and fluorescence mode, we demonstrate the capability of the method to reconstruct image from downsampled projection subsets. The result shows that the reconstruction image quality of the proposed method using 30 projections is comparable to that of FBP using 720 projections. The total acquisition procedure can be finished in a few seconds. The method also provides OPT with the remarkable capability to resist noises and artifacts. Projection image and fused image of zebrafish.

Fast Post-Processing Pipeline for Optical Projection Tomography

To improve the effectiveness and efficiency of optical projection tomography (OPT) 3-D reconstruction, we present a fast post-processing pipeline, including cropping, background subtraction, center of rotation (COR) correction, and 3-D reconstruction. Regarding to the COR correction, a novel algorithm based on interest point detection of sinogram is proposed by considering the principle of OPT imaging. Instead of locating the COR on single sinogram, we select equally spaced sinograms in the detected full range of specimen to make the located COR more convincing. The presented post-processing pipeline is implemented in a parallel manner and the experiments show that the average runtime for each image of size 1036 ×1360 ×400 pixels is less than 1 min. To quantify and compare the reconstructed results of different COR correction approaches, the coefficient of variation instead of variance is employed. The results indicate that the proposed COR correction outperforms the three traditional COR alignment approaches in terms of effectiveness and computational complexity.