[Show abstract][Hide abstract] ABSTRACT: The blood vessel density in a cancerous tissue sample may represent increased levels of tumor growth. However, identifying blood vessels in the histological (tissue) image is difficult and time-consuming and depends heavily on the observer’s experience. To overcome this drawback, computer-aided image analysis frameworks have been investigated in order to boost object identification in histological images. We present a novel algorithm to automatically abstract the salient regions in blood vessel images. Experimental results show that the proposed framework is capable of deriving vessel boundaries that are comparable to those demarcated manually, even for vessel regions with weak contrast between the object boundaries and background clutter.
[Show abstract][Hide abstract] ABSTRACT: Background and purpose:
The subventricular zone (SVZ) and the corpus callosum (CC) invasion status are separately associated with adverse prognosis for glioblastoma. We investigated the prognosis and progression patterns of glioblastoma with and without synchronous SVZ and CC (sSVZCC) invasion.
Material and methods:
Glioblastoma patients completing concurrent chemoradiotherapy with temozolomide were retrospectively categorized by the preoperative sSVZCC invasion status. The associations between sSVZCC invasion and the survival and progression patterns were analyzed.
In total, 108 patients, including 36 with sSVZCC invasion, were followed for a median period of 60.2 (range 34.2-86.3) months. The median overall survival (OS) of patients with and without sSVZCC were 18.6 and 26.4months, respectively (p=0.005). Using multivariate analyses with the factors of age, performance, surgery extent, and tumor size, sSVZCC invasion remained significant for a poor OS (hazard ratio, 1.96; 95% confidence interval, 1.19-3.21). The rates of progression at tumor bed, preoperative edematous areas, bilateral hemispheres, and ventricles for tumors with and without sSVZCC invasion were 75% and 63.9% (p=0.282), 41.7% and 9.7% (p<0.001), 47.2% and 13.9% (p<0.001), and 38.9% and 13.9% (p=0.006), respectively.
The sSVZCC invasion status determined the distinct prognosis and progression areas of glioblastoma, which suggests individualized radiotherapy and drug administration strategies.
No preview · Article · Dec 2015 · Radiotherapy and Oncology
[Show abstract][Hide abstract] ABSTRACT: Schizophrenia is a debilitating mental disorder that is associated with an impaired connection of cerebral white matter. Studies on patients with chronic and first-episode schizophrenia have found widespread white matter abnormalities. However, it is unclear whether the altered connections are inherent in or secondary to the disease. Here, we sought to identify white matter tracts with altered connections and to distinguish primary or secondary alterations among 74 fiber tracts across the whole brain using an automatic tractography-based analysis method. Thirty-one chronic, 25 first-episode patients with schizophrenia and 31 healthy controls were recruited to receive diffusion spectrum magnetic resonance imaging at 3T. Seven tracts were found to exhibit significant differences between the groups; they included the right arcuate fasciculus, bilateral fornices, left superior longitudinal fasciculus I, and fibers of the corpus callosum to the bilateral dorsolateral prefrontal cortices (DLPFC), bilateral temporal poles, and bilateral hippocampi. Post-hoc between-group analyses revealed that the connection of the callosal fibers to the bilateral DLPFC was significantly decreased in chronic patients but not in first-episode patients. In a stepwise regression analysis, the decline of the tract connection was significantly predicted by the duration of illness. In contrast, the remaining six tracts showed significant alterations in both first-episode and chronic patients and did not associate with clinical variables. In conclusion, reduced white matter connectivity of the callosal fibers to the bilateral DLPFC may be a secondary change that degrades progressively in the chronic stage, whereas alterations in the other six tracts may be inherent in the disease.
Full-text · Article · Oct 2015 · Schizophrenia Research
[Show abstract][Hide abstract] ABSTRACT: BACKGROUND
The common grading systems for female pattern hair loss (FPHL), such as Ludwig and Savin scales, are subjective to visual inspection. To provide a more objective evaluation of baldness, the authors have developed a method to calculate baldness quantitatively through a computer-aided imaging system (CAIS).OBJECTIVE
To investigate the use of CAIS on Chinese women with FPHL.MATERIALS AND METHODS
Thirty-eight Chinese women with FPHL (Savin Scale I-2 to II-2) were recruited. A total of 215 photographs were taken. The central balding areas (CBAs) were calculated after exposure correction by CAIS for comparison with clinical staging.RESULTSThe average CBA was 9,391.12 mm(2) in all patients, 3,828.84 mm(2) in Type I-2, 5,880.38 mm(2) in I-3, 8,267.44 mm(2) in I-4, 12,999.26 mm(2) in II-1, and 15,979.71 mm(2) in II-2. The values of CBA correlated with clinical staging using Savin scales. A 7.53% difference was found in the calculated CBA by exposure correction.CONCLUSION
The CAIS allows physicians to evaluate the severity of baldness more accurately through quantitative calculation, rather than qualitative visual observation. The values of the CBA measured by the CAIS, used in conjunction with the present grading systems, may be more precise and efficient to evaluate the severity of FPHL.
No preview · Article · Sep 2015 · Dermatologic Surgery
[Show abstract][Hide abstract] ABSTRACT: Patients with neuropsychiatric systemic lupus erythematosus (NPSLE) may exhibit corpus callosal atrophy and tissue alterations. Measuring the callosal volume and tissue integrity using diffusion tensor imaging (DTI) could help to differentiate patients with NPSLE from patients without NPSLE. Hence, this study aimed to use an automatic cell-competition algorithm to segment the corpus callosum and to investigate the effects of central nervous system (CNS) involvement on the callosal volume and tissue integrity in patients with SLE.
Twenty-two SLE patients with (N = 10, NPSLE) and without (N = 12, non-NPSLE) CNS involvement and 22 control subjects were enrolled in this study. For volumetric measurement, a cell-competition algorithm was used to automatically delineate corpus callosal boundaries based on a midsagittal fractional anisotropy (FA) map. After obtaining corpus callosal boundaries for all subjects, the volume, FA, and mean diffusivity (MD) of the corpus callosum were calculated. A post hoc Tamhane's T2 analysis was performed to statistically compare differences among NPSLE, non-NPSLE, and control subjects. A receiver operating characteristic curve analysis was also performed to compare the performance of the volume, FA, and MD of the corpus callosum in differentiating NPSLE from other subjects.
Patients with NPSLE had significant decreases in volume and FA but an increase in MD in the corpus callosum compared with control subjects, whereas no significant difference was noted between patients without NPSLE and control subjects. The FA was found to have better performance in differentiating NPSLE from other subjects.
A cell-competition algorithm could be used to automatically evaluate callosal atrophy and tissue alterations. Assessments of the corpus callosal volume and tissue integrity helped to demonstrate the effects of CNS involvement in patients with SLE.
No preview · Article · Aug 2015 · Journal of computer assisted tomography
[Show abstract][Hide abstract] ABSTRACT: The most common cancer in children is acute lymphoblastic leukemia (ALL) and it had high cure rate, especially for B-precursor ALL. However, relapse due to drug resistance and overdose treatment reach the limitations in patient managements. In this study, integration of gene expression microarray data, logistic regression, analysis of microarray (SAM) method, and gene set analysis were performed to discover treatment response associated pathway-based signatures in the original cohort. Results showed that 3772 probes were significantly associated with treatment response. After pathway analysis, only apoptosis pathway had significant association with treatment response. Apoptosis pathway signature (APS) derived from 15 significantly expressed genes had 88% accuracy for treatment response prediction. The APS was further validated in two independent cohorts. Results also showed that APS was significantly associated with induction failure time (adjusted hazard ratio [HR] = 1.60, 95% confidence interval [CI] = [1.13, 2.27]) in the first cohort and significantly associated with event-free survival (adjusted HR = 1.56, 95% CI = [1.13, 2.16]) or overall survival in the second cohort (adjusted HR = 1.74, 95% CI = [1.24, 2.45]). APS not only can predict clinical outcome, but also provide molecular guidance of patient management.
No preview · Article · Jul 2015 · American Journal of Cancer Research
[Show abstract][Hide abstract] ABSTRACT: Lung adenocarcinoma is often diagnosed at an advanced stage with poor prognosis. Patients with different clinical outcomes may have similar clinico-pathological characteristics. The results of previous studies for biomarkers for lung adenocarcinoma have generally been inconsistent and limited in clinical application. In this study, we used inverse-variance weighting to combine the hazard ratios for the four datasets and performed pathway analysis to identify prognosis-associated gene signatures. A total of 2,418 genes were found to be significantly associated with overall survival. Of these, a 21-gene signature in the HMGB1/RAGE signalling pathway and a 31-gene signature in the clathrin-coated vesicle cycle pathway were significantly associated with prognosis of lung adenocarcinoma across all four datasets (all p-values<0.05, log-rank test). We combined the scores for the three pathways to derive a combined pathway-based risk (CPBR) score. Three pathway-based signatures and CPBR score also had more predictive power than single genes. Finally, the CPBR score was validated in two independent cohorts (GSE14814 and GSE13213 in the GEO database) and had significant adjusted hazard ratios 2.72 (p-value<0.0001) and 1.71 (p-value<0.0001), respectively. These results could provide a more complete picture of the lung cancer pathogenesis.
[Show abstract][Hide abstract] ABSTRACT: Background
This paper proposes a semantic segmentation algorithm that provides the spatial distribution patterns of pulmonary ground-glass nodules with solid portions in computed tomography (CT) images.
The proposed segmentation algorithm, anatomy packing with hierarchical segments (APHS), performs pulmonary nodule segmentation and quantification in CT images. In particular, the APHS algorithm consists of two essential processes: hierarchical segmentation tree construction and anatomy packing. It constructs the hierarchical segmentation tree based on region attributes and local contour cues along the region boundaries. Each node of the tree corresponds to the soft boundary associated with a family of nested segmentations through different scales applied by a hierarchical segmentation operator that is used to decompose the image in a structurally coherent manner. The anatomy packing process detects and localizes individual object instances by optimizing a hierarchical conditional random field model. Ninety-two histopathologically confirmed pulmonary nodules were used to evaluate the performance of the proposed APHS algorithm. Further, a comparative study was conducted with two conventional multi-label image segmentation algorithms based on four assessment metrics: the modified Williams index, percentage statistic, overlapping ratio, and difference ratio.
Under the same framework, the proposed APHS algorithm was applied to two clinical applications: multi-label segmentation of nodules with a solid portion and surrounding tissues and pulmonary nodule segmentation. The results obtained indicate that the APHS-generated boundaries are comparable to manual delineations with a modified Williams index of 1.013. Further, the resulting segmentation of the APHS algorithm is also better than that achieved by two conventional multi-label image segmentation algorithms.
The proposed two-level hierarchical segmentation algorithm effectively labelled the pulmonary nodule and its surrounding anatomic structures in lung CT images. This suggests that the generated multi-label structures can potentially serve as the basis for developing related clinical applications.
Preview · Article · May 2015 · BioMedical Engineering OnLine
[Show abstract][Hide abstract] ABSTRACT: Background: The conventional approach to evaluate female pattern hair loss (FPHL) is to visually inspect and score images of balding area (BA). However, visual estimates vary widely among different physicians, and may hinder objective assessment of hair loss and subsequent treatment response. For this reason, we propose a quantitative method using a computer-aided imaging system to help physicians evaluate the severity of FPHL clinically. Methods: We use a series of digital image processing techniques to measure the width of central balding area of FPHL. A total of 184 photos were collected form 33 Chinese women with FPHL (stages I-2 to II-2 on the Savin scale). Each photograph underwent standardized exposure correction. The balding areas were detected through this computer system and then transformed into an equivalent ellipse by principal component analysis. The width of ellipse [balding width (BW)] was measured. Spearman's rank correlation was used to detect the correlation between our measurements and clinical staging. Results: Exposure correction resulted in a 16.97% (|BWcorrected-BWoriginal|/BWcorrected) difference in BW.[U+200F] The average BW was 54.98mm in all patients, 25.79mm in type I-2 patients, 37.41mm in I-3, 54.08mm in I-4, 72.10mm in II-1, and 85.53mm in II-2. The values of BW were correlated with Savin scale stages clinically (r BW=0.967), which was significant statistically (p<0.05). Conclusion: A computer-aided imaging system could be a useful tool to assist physicians to evaluate the balding area more precisely for clinical staging in FPHL. The BW instead of the balding area is simple to use clinically to represent the severity of FPHL.
Full-text · Article · Feb 2015 · Dermatologica Sinica
[Show abstract][Hide abstract] ABSTRACT: Superparamagnetic nanoparticles (Fe3O4, SPIO) have been used as magnetic resonance imaging enhancers for years. However, bio-safety issues concerning nanoparticles remain largely unexplored. Of particular concern is the possible cellular impact of nanoparticles during SPIO uptake and subsequent oxidative stress. SPIO causes cell death by apoptosis via a little understood mitochondrial pathway. To more closely examine this process, three kinds of cells—3T3, RAW264.7, and MCF7—were treated with SPIO coated with polyethylene glycol (SPIO-PEG) and monitored by transmission electron microscopy (TEM), using cytotoxicity evaluation, mitochondrial activity, reactive oxygen species (ROS) generation, and Annexin V assay. TEM revealed that SPIO-PEG nanoparticles surrounded the cellular endosome membrane, creating a bulge in the endosome. Compared to 3T3 cells, greater numbers of SPIO-PEG nanoparticles infiltrated the mitochondria of RAW264.7 and MCF7 cells. SPIO-PEG residency is associated with boosted ROS, with elevated levels of mitochondrial activity, and advancement of cell apoptosis. Furthermore, correlation analysis showed that a polynomial model demonstrates a better fit than a linear model in MCF7, implying that cytotoxicity may have alternative impacts on cell death at different concentrations. Thus, we believe that MCF7 cell death results from the apoptosis pathway triggered by mitochondria, and we find lower cytotoxicity in 3T3. We propose that optimal levels of SPIO-PEG nanoparticles lead to increased levels of ROS and a resulting oxidative stress environment which will kill only cancer cells while sparing normal cells. This finding has great potential for use in cancer therapies in the future.
No preview · Article · Feb 2015 · Journal of Nanoparticle Research
[Show abstract][Hide abstract] ABSTRACT: Purpose:
Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies.
The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries.
Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed.
Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.
Full-text · Article · Jan 2015 · BioMed Research International
[Show abstract][Hide abstract] ABSTRACT: Recent studies suggest that structural and functional alterations of the language network are associated with auditory verbal hallucinations (AVHs) in schizophrenia. However, the ways in which the underlying structure and function of the network are altered and how these alterations are related to each other remain unclear. To elucidate this, we used diffusion spectrum imaging (DSI) to reconstruct the dorsal and ventral pathways and employed functional magnetic resonance imaging (fMRI) in a semantic task to obtain information about the functional activation in the corresponding regions in 18 patients with schizophrenia and 18 matched controls. The results demonstrated decreased structural integrity in the left ventral, right ventral and right dorsal tracts, and decreased functional lateralization of the dorsal pathway in schizophrenia. There was a positive correlation between the microstructural integrity of the right dorsal pathway and the functional lateralization of the dorsal pathway in patients with schizophrenia. Additionally, both functional lateralization of the dorsal pathway and microstructural integrity of the right dorsal pathway were negatively correlated with the scores of the delusion/hallucination symptom dimension. Our results suggest that impaired structural integrity of the right dorsal pathway is related to the reduction of functional lateralization of the dorsal pathway, and these alterations may aggravate AVHs in schizophrenia.
[Show abstract][Hide abstract] ABSTRACT: Harris corner detectors, which depend on strong invariance and a local autocorrelation function, display poor detection performance for infrared (IR) images with low contrast and nonobvious edges. In addition, feature points detected by Harris corner detectors are clustered due to the numerous nonlocal maxima. This paper proposes a modified Harris corner detector that includes two unique steps for processing IR images in order to overcome the aforementioned problems. Image contrast enhancement based on a generalized form of histogram equalization (HE) combined with adjusting the intensity resolution causes false contours on IR images to acquire obvious edges. Adaptive nonmaximal suppression based on eliminating neighboring pixels avoids the clustered features. Preliminary results show that the proposed method can solve the clustering problem and successfully identify the representative feature points of IR breast images.
[Show abstract][Hide abstract] ABSTRACT: This work presents a new method for segmenting coronary arteries automatically in computed tomography angiography (CTA) data sets. The method automatically isolates heart and coronary arteries from surrounding structures and search for the probable location of coronary arteries by 3D region growing. Based on the dilation of the probable location, discrete wavelet transformation (DWT) and λ - mean operation complete accurate detection of coronary arties. Finally, the proposed method is tested on clinical CTA data-sets. The results on clinical datasets show that the proposed method is able to extract each branch of arteries when comparing to commercial software GE Healthcare and delineated ground truth.
No preview · Article · Jun 2014 · Journal of Medical Systems
[Show abstract][Hide abstract] ABSTRACT: We present the first experimental results of time-resolved diffuser-aided diffuse optical imaging (DADOI) method in this paper. A self-manufactured diffuser plate was inserted between the optode and the surface of a scattering medium. The diffuser was utilized to enhance the multiple scattering that destroys the image information for baseline measurement of turbid medium. Therefore, the abnormality can be detected with the modified optical density calculation. The time-domain DADOI method can provide better imaging contrast and simpler imaging than the conventional diffuse optical tomography measurement. Besides, it also reveals rich depth information with temporal responses. Therefore, the DADOI offers a great potential to detect the breast tumor and chemotherapy monitoring in clinical diagnosis.
No preview · Article · Apr 2014 · Journal of Biomedical Optics
[Show abstract][Hide abstract] ABSTRACT: A whole breast ultrasound tumor detection algorithm, which could help physicians determine and diagnose, has been proposed. First of all, we employ the multi-scale blob detection. Secondly, we discard most unlikely lesions by ultrasound confidence maps and sheet detection, according to the prior knowledge of breast anatomy. After discarding unlikely blob structures, we collect the survival blob-like structures from the results of four passes of multi-scale blob detection in a single set. The features of blobness, size, and probability of being at muscle layer of the all detected blob-like candidates are used in a classification process for the differentiation of true lesions from negative ones in the collection set. Mutual information based feature selection (MIFS) procedure is applied to select three most effective features for the purpose of dimension reduction with the aid of logistic regression classifier and the process of leave-one-out cross-validation (LOO-CV) method. 49 datasets were acquired from 29 patients, which contain 86 lesions in total. According to the area under the ROC curve (AUC), the technique shows promising future for computer aided detection and the superior results when compared to Moon's method.
[Show abstract][Hide abstract] ABSTRACT: This paper proposes a general boundary delineation method for 2D serial US images by modeling the spatial-temporal dynamics and generating the object boundary in each slice under the cell-based MAP scheme. The modeling of the spatial-temporal dynamics can serve as a prior to guide the cell-based MAP process and potentially maintain the contextual coherence. Experiments have been conducted on 8 sets of compression breast series and 5 sets of freehand breast acquisitions. The computer-generated results by our algorithm are compared to manual delineations prepared by experts. The experimental results suggest that the boundaries of the proposed method are not significantly different to manual outlines and are quite stable in terms of reproducibility.
[Show abstract][Hide abstract] ABSTRACT: Perfusion computed tomography (CT) has been widely used to assess the response of lung cancer treatment. However,
the respiratory motion has become the major obstacle to the pixel-based time-series analyses. To minimize the effect of
respiratory motion and investigate the feasibility of perfusion CT for prediction of tumor response and prognosis of non-small cell lung cancer, an image registration framework is proposed by unifying a virtual 3D local rigid alignment and 3D global non-rigid alignment. The basic idea is to use the perfusion CT data and routine whole-lung CT data,
respectively. To realize this idea, maximum intensity projection (MIP) of the time series perfusion CT images is first
generated, followed by decomposing the MIP image into region of interest (ROI), which is located on a lung nodule. For the ROI, affine transformation model based on mutual information is performed to estimate the virtual three dimensional linear deformations. Following that, the 3D thin plate spline (TPS) is carried out to establish the pixel correspondence between the paired volumetric CT data. The control points for the TPS are global feature points chosen from the boundary of whole lung, which are automatically derived by using the iterative closest point (ICP) matching Algorithm. The proposed algorithm has been evaluated both qualitatively and quantitatively on real lung perfusion CT datasets. From the time-intensity curves and perfusion parameters, the experiment results suggest that the findings on perfusion CT images obtained after treatment may be considered as a significant predictor of lung cancer.
[Show abstract][Hide abstract] ABSTRACT: Image segmentation for the demarcation of pulmonary nodules in CT images is intrinsically an arduous task. The
difficulty can be summarized into two aspects. Firstly, lung tumor can be various in terms of physical densities in
pulmonary regions, implying the different interpretation as a mixture of GGO and solid nodules. Hence, processing of
lung CT images may generally encounter tissue inhomogeneous problem. The second factor that complicates the task of
nodule demarcation is the irregular shapes that most nodules are directly connected to other structures sharing the similar
density profile. In this paper, an image segmentation framework is proposed by unifying the techniques of statistical
region merging and conditional random field (CRF) with graph cut optimization to address the difficult problem of GGO
nodules quantification in CT images. Different from traditional segmentation methods that use pixel-based approach
such as region growing and morphological constraints, we employ a hierarchical segmentation tree to alleviate the effect
of inhomogeneous attenuation. In addition to building perceptual prominent regions, we perform inference in CRF model
based on restricting the pool of segmented regions. Following that, an inference CRF model is carried out to detect and
localize individual object instances in CT images. The proposed algorithm is evaluated with four sets of manual
delineations on 77 lung CT images. Incorporating with the efficiency and accuracy of pulmonary nodules segmentation
method proposed in this paper, a computer aided system is hence feasible to develop related clinical application.