Jie-Zhi Cheng

CENTS: cortical enhanced neonatal tissue segmentation.

The acquisition of high‐quality magnetic resonance (MR) images of neonatal brains is largely hampered by their characteristically small head size and insufficient tissue contrast. As a result, subsequent image processing and analysis, especially brain tissue segmentation, are often affected. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by augmenting signal‐to‐noise ratio and spatial resolution without lengthening data acquisition time. In addition, a specialized hybrid atlas‐based tissue segmentation algorithm is developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet‐like cortical gray matter (GM) structures in the to‐be‐segmented neonatal image with a Hessian filter for generation of a cortical GM confidence map. A neonatal population atlas is then generated by averaging the presegmented images of a population, weighted by their cortical GM similarity with respect to the to‐be‐segmented image. Finally, the neonatal population atlas is combined with the GM confidence map, and the resulting enhanced tissue probability maps for each tissue form a hybrid atlas is used for atlas‐based segmentation. Various experiments are conducted to compare the segmentations of the proposed method with manual segmentation (on both images acquired with a dedicated phased array coil and a conventional volume coil), as well as with the segmentations of two population‐atlas‐based methods. Results show the proposed method is capable of segmenting the neonatal brain with the best accuracy, and also preserving the most structural details in the cortical regions. Hum Brain Mapp, 2011.

Computer-aided US Diagnosis of Breast Lesions by Using Cell-based Contour Grouping

PURPOSE To develop a computer-aided diagnostic algorithm with automatic boundary delineation for differential diagnosis of benign and malignant breast lesions at ultrasonography (US) and investigate the effect of boundary quality on the performance of a computer-aided diagnostic algorithm. MATERIALS AND METHODS This was an institutional review board-approved retrospective study with waiver of informed consent. A cell-based contour grouping (CBCG) segmentation algorithm was used to delineate the lesion boundaries automatically. Seven morphologic features were extracted. The classifier was a logistic regression function. Five hundred twenty breast US scans were obtained from 520 subjects (age range, 15-89 years), including 275 benign (mean size, 15 mm; range, 5-35 mm) and 245 malignant (mean size, 18 mm; range, 8-29 mm) lesions. The newly developed computer-aided diagnostic algorithm was evaluated on the basis of boundary quality and differentiation performance. The segmentation algorithms and features in two conventional computer-aided diagnostic algorithms were used for comparative study. RESULTS The CBCG-generated boundaries were shown to be comparable with the manually delineated boundaries. The area under the receiver operating characteristic curve (AUC) and differentiation accuracy were 0.968 +/- 0.010 and 93.1% +/- 0.7, respectively, for all 520 breast lesions. At the 5% significance level, the newly developed algorithm was shown to be superior to the use of the boundaries and features of the two conventional computer-aided diagnostic algorithms in terms of AUC (0.974 +/- 0.007 versus 0.890 +/- 0.008 and 0.788 +/- 0.024, respectively). CONCLUSION The newly developed computer-aided diagnostic algorithm that used a CBCG segmentation method to measure boundaries achieved a high differentiation performance.

ACCOMP: Augmented cell competition algorithm for breast lesion demarcation in sonography

PURPOSE Fully automatic and high-quality demarcation of sonographical breast lesions remains a far-reaching goal. This article aims to develop an image segmentation algorithm that provides quality delineation of breast lesions in sonography with a simple and friendly semiautomatic scheme. METHODS A data-driven image segmentation algorithm, named as augmented cell competition (ACCOMP) algorithm, is developed to delineate breast lesion boundaries in ultrasound images. Inspired by visual perceptual experience and Gestalt principles, the ACCOMP algorithm is constituted of two major processes, i.e., cell competition and cell-based contour grouping. The cell competition process drives cells, i.e., the catchment basins generated by a two-pass watershed transformation, to merge and split into prominent components. A prominent component is defined as a relatively large and homogeneous region circumscribed by a perceivable boundary. Based on the prominent component tessellation, cell-based contour grouping process seeks the best closed subsets of edges in the prominent component structure as the desirable boundary candidates. Finally, five boundary candidates with respect to five devised boundary cost functions are suggested by the ACCOMP algorithm for user selection. To evaluate the efficacy of the ACCOMP algorithm on breast lesions with complicated echogenicity and shapes, 324 breast sonograms, including 199 benign and 125 malignant lesions, are adopted as testing data. The boundaries generated by the ACCOMP algorithm are compared to manual delineations, which were confirmed by four experienced medical doctors. Four assessment metrics, including the modified Williams index, percentage statistic, overlapping ratio, and difference ratio, are employed to see if the ACCOMP-generated boundaries are comparable to manual delineations. A comparative study is also conducted by implementing two pixel-based segmentation algorithms. The same four assessment metrics are employed to evaluate the boundaries generated by two conventional pixel-based algorithms based on the same set of manual delineations. RESULTS The ACCOMP-generated boundaries are shown to be comparable to the manual delineations. Particularly, the modified Williams indices of the boundaries generated by the ACCOMP algorithm and the first and second pixel-based algorithms are 1.069 +/- 0.024, 0.935 +/- 0.024, and 0.579 +/- 0.013, respectively. If the modified Williams index is greater than or equal to 1, the average distance between the computer-generated boundaries and manual delineations is deemed to be comparable to that between the manual delineations. CONCLUSIONS The boundaries derived by the ACCOMP algorithm are shown to reasonably demarcate sonographic breast lesions, especially for the cases with complicated echogenicity and shapes. It suggests that the ACCOMP-generated boundaries can potentially serve as the basis for further morphological or quantitative analysis.

Automated Delineation of Calcified Vessels in Mammography by Tracking With Uncertainty and Graphical Linking Techniques

As a potential biomarker for womens cardiovascular and chronic kidney diseases, breast arterial calcification (BAC) in mammography has become an emerging research topic in recent years. To provide more objective measurement for vascular structures with calcium depositions in mammography, a new computerized method is introduced in this paper to delineate the calcified vessels. Specifically, we leverage two underlying cues, namely calcification and vesselness, into a multiple seeded tracking with uncertainty scheme. This new vessel-tracking scheme generates plenty of sampling paths to describe the complicated topology of the vascular structures with calcium depositions. A compiling and linking process is further carried out to organize the sampling paths together to be the vessel segments that likely belong to the same vessel tract. The proposed method has been evaluated on 63 mammograms, by comparison with manual delineations from two experts using various assessment metrics. The experiment results confirm the efficacy and stability of the proposed method, and also indicate that the proposed method can be potentially used as a convenient BAC measurement tool in replacement of the trivial and tedious manual delineation tasks.

Detection of Arterial Calcification in Mammograms by Random Walks

A fully automatic algorithm is developed for breast arterial calcification extraction in mammograms. This algorithm is implemented in two major steps: a random-walk based tracking step and a compiling and linking step. With given seeds from detected calcification points, the tracking algorithm traverses the vesselness map by exploring the uncertainties of three tracking factors, i.e., traversing direction, jumping distance, and vesselness value, to generate all possible sampling paths. The compiling and linking algorithm further organizes and groups all sampling paths into calcified vessel tracts. The experimental results show that the performance of the proposed automatic calcification extraction algorithm is statistically close to that obtained by manual delineations.

Automated DNA fiber tracking and measurement

The rapid assessment of how cells respond to pathologic, biological, environmental, and endogenous agents is critical for understanding how such responses may increase genomic instability, disease development and, ultimately, affect quality of life. Recently, we have applied the technique of DNA fiber analysis to increase our understanding of how DNA damaging agents influence DNA replication [1]. The major drawback to this technique, however, is the length of time needed to manually assess the replication dynamics, i.e., 40 hours per experimental condition. We introduce in this paper an automatic and effective image analysis framework for accurate quantification of various replication tracks for high throughput DNA analysis, called Computer Assisted Selection and Analysis (CASA). This algorithm is implemented in two major steps: random-walk-based DNA fiber tracking and pattern-recognition-based measurement of the replication tracks. Experimental results show that the proposed method can yield results which are highly consistent with manual analysis and needs only 160-fold less processing time, without manual involvement.

Automatic Segmentation of the Corpus Callosum Using a Cell-Competition Algorithm: Diffusion Tensor Imaging-Based Evaluation of Callosal Atrophy and Tissue Alterations in Patients With Systemic Lupus Erythematosus.

Objective 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. Methods 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 Tamhanes 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. Results 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. Conclusions 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.

Cell-based graph cut for segmentation of 2D/3D sonographic breast images

Boundary delineation is the fundamental basis of many sonographic image analyses. In sonographic breast lesion images, its complicated and time consuming for physicians to delineate the lesion boundaries. When it comes to three dimensional sonographic breast lesions image, delineation of lesion boundary becomes much more complicated. Taking advantage of cell competition algorithm along with its good region structure, generated cells can be served as elegant nodes for graph cut. Further, a similar weight function plays an important role in the estimation of lesion boundary to avoid visible weak edge and isolated node in graph cut. The integration of cell competition and graph cut can be intuitively implemented in three dimensional images, in addition to the reduction of computational time. With efficiency and accuracy of lesion detection, a computer aided system was therefore developed to fulfill clinical applications.

Identification of breast vascular calcium deposition in digital mammography by linear structure analysis

Computerized detection of vascular calcium depositions in mamagraphy is a new research topics, which is driven by the clinical hypothesis of the association with many related cardiovascular diseases. In several previous studies [7, 9], calcification cue plays a very important role in the computerized analysis. We observe that vascular calcium depositions can be identified with high confidence if they appear in a bright railway pattern. Accordingly, a linear structure analysis method is introduced in this study to detect most true calcifications and also keep the false positives as little as possible. The proposed method is tested with 40 mammograms and achieves performance of 93.8±1.3% in sensitivity and 84.7±3.9% in specificity. The output of this linear structure analysis may provide more reliable calcification cue for the subsequent vessel tracking process, which will be investigated in the future.

Stochastic region competition algorithm for Doppler sonography segmentation

PURPOSE A probabilistic image segmentation algorithm called stochastic region competition is proposed for performing Doppler sonography segmentation. METHODS The image segmentation is conducted by maximizing a posteriori that models histogram likelihood, gradient likelihood, and a spatial prior. The optimization is done by a modified expectation and maximization (EM) method that aims to improve computation efficiency and avoid local optima. RESULTS The algorithm was tested on 155 color Doppler sonograms and compared with manual delineations. The qualitative assessment shows that our algorithm is able to segment mass lesions under the condition of low image quality and the interference of the color-encoded Doppler information. The quantitative assessment analysis shows that the average distance between the algorithm-generated boundaries and manual delineations is statistically comparable to the variability of manual delineations. The ratio of the overlapping area between the algorithm-generated boundaries and manual delineations is also comparable to that between different sets of manual delineations. A reproductivity test was conducted to confirm that the result is statistically reproducible. CONCLUSIONS The algorithm can be used to perform Doppler sonography segmentation and to replace the tedious manual delineation task in clinical application.