Bottom Deposition Enables Stable All-Solid-State Batteries with Ultrathin Lithium Metal Anode.

Modern strides in energy storage underscore the significance of all-solid-state batteries (ASSBs) predicated on solid electrolytes and lithium (Li) metal anodes in response to the demand for safer batteries. Nonetheless, ASSBs are often beleaguered by non-uniform Li deposition during cycling, leading to compromised cell performance from internal short circuits and hindered charge transfer. In this study, the concept of "bottom deposition" is introduced to stabilize metal deposition based on the lithiophilic current collector and a protective layer composed of a polymeric binder and carbon black. The bottom deposition, wherein Li plating ensues between the protective layer and the current collector, circumvents internal short circuits and facilitates uniform volumetric changes of Li. The prepared functional binder for the protective layer presents outstanding mechanical robustness and adhesive properties, which can withstand the volume expansion caused by metal growth. Furthermore, its excellent ion transfer properties promote uniform Li bottom deposition even under a current density of 6 mA·cm-2 . Also, scanning electron microscopy analysis reveals a consistent plating/stripping morphology of Li after cycling. Consequently, the proposed system exhibits enhanced electrochemical performance when assessed within the ASSB framework, operating under a configuration marked by a high Li utilization rate reliant on an ultrathin Li.

Are shape morphologies associated with survival? A potential shape-based biomarker predicting survival in lung cancer.

PURPOSE: Imaging biomarkers (IBMs) are increasingly investigated as prognostic indicators. IBMs might be capable of assisting treatment selection by providing useful insights into tumor-specific factors in a non-invasive manner. METHODS: We investigated six three-dimensional shape-based IBMs: eccentricities between (I) intermediate-major axis (Eimaj), (II) intermediate-minor axis (Eimin), (III) major-minor axis (Emj-mn) and volumetric index of (I) sphericity (VioS), (II) flattening (VioF), (III) elongating (VioE). Additionally, we investigated previously established two-dimensional shape IBMs: eccentricity (E), index of sphericity (IoS), and minor-to-major axis length (Mn_Mj). IBMs were compared in terms of their predictive performance for 5-year overall survival in two independent cohorts of patients with lung cancer. Cohort 1 received surgical excision, while cohort 2 received radiation therapy alone or chemo-radiation therapy. Univariate and multivariate survival analyses were performed. Correlations with clinical parameters were evaluated using analysis of variance. IBM reproducibility was assessed using concordance correlation coefficients (CCCs). RESULTS: E was associated with reduced survival in cohort 1 (hazard ratio [HR]: 0.664). Eimin and VioF were associated with reduced survival in cohort 2 (HR 1.477 and 1.701). VioS was associated with reduced survival in cohorts 1 and 2 (HR 1.758 and 1.472). Spherical tumors correlated with shorter survival durations than did irregular tumors (median survival difference: 1.21 and 0.35 years in cohorts 1 and 2, respectively). VioS was a significant predictor of survival in multivariate analyses of both cohorts. All IBMs showed good reproducibility (CCC ranged between 0.86-0.98). CONCLUSIONS: In both investigated cohorts, VioS successfully linked shape morphology to patient survival.

Automated integrated system for stained neuron detection: An end-to-end framework with a high negative predictive rate.

BACKGROUND AND OBJECTIVE: Mapping the architecture of the brain is essential for identifying the neural computations that affect behavior. Traditionally in histology, stained objects in tissue slices are hand-marked under a microscope in a manually intensive, time-consuming process. An integrated hardware and software system is needed to automate image acquisition, image processing, and object detection. Such a system would enable high throughput tissue analysis to rapidly map an entire brain. METHODS: We demonstrate an automated system to detect neurons using a monkey brain slice immunohistochemically stained for retrogradely labeled neurons. The proposed system obtains a reconstructed image of the sample, and stained neurons are detected in three steps. First, the reconstructed image is pre-processed using adaptive histogram equalization. Second, candidates for stained neurons are segmented from each region via marker-controlled watershed transformation (MCWT) using maximally stable extremal regions (MSERs). Third, the candidates are categorized as neurons or non-neurons using deep transfer learning via pre-trained convolutional neural networks (CNN). RESULTS: The proposed MCWT algorithm was compared qualitatively against MorphLibJ and an IHC analysis tool, while our unified classification algorithm was evaluated quantitatively using ROC analyses. The proposed classification system was first compared with five previously developed layers (AlexNet, VGG-16, VGG-19, GoogleNet, and ResNet). A comparison with conventional multi-stage frameworks followed using six off-the-shelf classifiers [Bayesian network (BN), support vector machines (SVM), decision tree (DT), bagging (BAG), AdaBoost (ADA), and logistic regression (LR)] and two descriptors (LBP and HOG). The system achieved a 0.918 F1-score with an 86.6% negative prediction value. Remarkably, other metrics such as precision, recall, and F-scores surpassed the 90% threshold compared to traditional methods. CONCLUSIONS: We demonstrate a fully automated, integrated hardware and software system for rapidly acquiring focused images and identifying neurons from a stained brain slice. This system could be adapted for the identification of stained features of any biological tissue.

Early Systemic Failure After Preoperative Chemoradiotherapy for the Treatment of Patients With Rectal Cancer.

PURPOSE: Distant metastasis can occur early after neoadjuvant chemoradiotherapy (CRT) in patients with rectal cancer. This study was conducted to evaluate the clinical characteristics of patients who developed early systemic failure. METHODS: The patients who underwent neoadjuvant CRT for a rectal adenocarcinoma between June 2007 and July 2015 were included in this study. Patients who developed distant metastasis within 6 months after CRT were identified. We compared short- and long-term clinicopathologic outcomes of patients in the early failure (EF) group with those of patients in the control group. RESULTS: Of 107 patients who underwent neoadjuvant CRT for rectal cancer, 7 developed early systemic failure. The lung was the most common metastatic site. In the EF group, preoperative carcinoembryonic antigen was higher (5 mg/mL vs. 2 mg/mL, P = 0.010), and capecitabine as a sensitizer of CRT was used more frequently (28.6% vs. 3%, P = 0.002). Of the 7 patients in the EF group, only 4 underwent a primary tumor resection (57.1%), in contrast to the 100% resection rate in the control group (P < 0.001). In terms of pathologic outcomes, ypN and TNM stages were more advanced in the EF group (P < 0.001 and P = 0.047, respectively), and numbers of positive and retrieved lymph nodes were much higher (P < 0.001 and P = 0.027, respectively). CONCLUSION: Although early distant metastasis after CRT for rectal cancer is very rare, patients who developed early metastasis showed a poor nodal response with a low primary tumor resection rate and poor oncologic outcomes.

Optimal depth estimation using modified Kalman filter in the presence of non-Gaussian jitter noise.

The consideration of the noise that affects 3D shape recovery is becoming very important for accurate shape reconstruction. In Shape from Focus, when 2D image sequences are obtained, mechanical vibrations, referred as jitter noise, occur randomly along the z-axis, in each step. To model the noise for real world scenarios, this article uses Lévy distribution for noise profile modeling. Next, focus curves acquired by one of focus measure operators are modeled as Gaussian function to consider the effects of the jitter noise. Finally, since conventional Kalman filter provides good output under Gaussian noise only, a modified Kalman filter, as proposed method, is used to remove the jitter noise. Experiments are carried out using synthetic and real objects to show the effectiveness of the proposed method.

Computer-assisted subtyping and prognosis for non-small cell lung cancer patients with unresectable tumor.

BACKGROUND: The histological classification or subtyping of non-small cell lung cancer is essential for systematic therapy decisions. Differentiating between the two main subtypes of pulmonary adenocarcinoma and squamous cell carcinoma highlights the considerable differences that exist in the prognosis of patient outcomes. Physicians rely on a pathological analysis to reveal these phenotypic variations that requires invasive methods, such as biopsy and resection sample, but almost 70% of tumors are unresectable at the point of diagnosis. METHOD: A computational method that fuses two frameworks of computerized subtyping and prognosis was proposed, and it was validated against publicly available dataset in The Cancer Imaging Archive that consisted of 82 curated patients with CT scans. The accuracy of the proposed method was compared with the gold standard of pathological analysis, as defined by theInternational Classification of Disease for Oncology (ICD-O). A series of survival outcome test cases were evaluated using the Kaplan-Meier estimator and log-rank test (p-value) between the computational method and ICD-O. RESULTS: The computational method demonstrated high accuracy in subtyping (96.2%) and good consistency in the statistical significance of overall survival prediction for adenocarcinoma and squamous cell carcinoma patients (p < 0.03) with respect to its counterpart pathological subtyping (p < 0.02). The degree of reproducibility between prognosis taken on computational and pathological subtyping was substantial with an averaged concordance correlation coefficient (CCC) of 0.9910. CONCLUSION: The findings in this study support the idea that quantitative analysis is capable of representing tissue characteristics, as offered by a qualitative analysis.

Removal of jitter noise in 3D shape recovery from image focus by using Kalman filter.

In regard to Shape from Focus, one critical factor impacting system application is mechanical vibration of the translational stage causing jitter noise along the optical axis. This noise is not detectable by simply observing the image. However, when focus measures are applied, inaccuracies in the depth occur. In this article, jitter noise and focus curves are modeled by Gaussian distribution and quadratic function, respectively. Then Kalman filter is designed and applied to eliminate this noise in the focus curves, as a post-processing step after the focus measure application. Experiments are implemented with simulated objects and real objects to show usefulness of proposed algorithm.

Deciphering unclassified tumors of non-small-cell lung cancer through radiomics.

BACKGROUND: Tumors are highly heterogeneous at the phenotypic, physiologic, and genomic levels. They are categorized in terms of a differentiated appearance under a microscope. Non-small-cell lung cancer tumors are categorized into three main subgroups: adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In approximately 20% of pathology reports, they are returned unclassified or classified as not-otherwise-specified (NOS) owing to scant materials or poor tumor differentiation. METHOD: We present a radiomic interrogation of molecular spatial variations to decode unclassified NOS tumor architecture quantitatively. Twelve spatial descriptors with various displacements and directions were extracted and profiled with respect to the subgroups. The profiled descriptors were used to decipher the NOS tumor morphologic clues from the imaging phenotype perspective. This profiler was built as an extended version of a conventional support-vector-machine classifier, wherein a genetic algorithm and correlation analysis were embedded to define the molecular signatures of poorly differentiated tumors using well-differentiated-tumor information. RESULTS: Sixteen multi-class classifier models with an 81% average accuracy and descriptor subset size ranging from 12 to 144 were reported. The average area under the curve was 86.3% at a 95% confidence interval and a 0.03-0.08 standard error. Correlation analysis returned an unclassified NOS membership matrix with respect to the cell-architecture similarity score for the subgroups. The best model demonstrated 53% NOS reduction. CONCLUSION: The membership matrix is expected to assist pathologists and oncologists in cases of unresectable tumors or scant biopsy materials for histological subtyping and cancer therapy.

Robust focus measure operator using adaptive log-polar mapping for three-dimensional shape recovery.

Shape from focus (SFF) is a passive optical technique that reconstructs object shape from a sequence of image taken at different focus levels. In SFF techniques, computing focus measurement for each pixel in the image sequence, through a focus measure operator, is the fundamental step. Commonly used focus measure operators compute focus quality in Cartesian space and suffer from erroneous focus quality and lack in robustness. Thus, they provide erroneous depth maps. In this paper, we introduce a new focus measure operator that computes focus quality in log-polar transform (LPT) Properties of LPT, such as biological inspiration, data selection, and edge invariance, enable computation of better focus quality in the presence of noise. Moreover, instead of using a fixed patch of the image, we suggest the use of an adaptive window. The focus quality is assessed by computing variation in LPT. The effectiveness of the proposed technique is evaluated by conducting experiments using image sequences of different simulated and real objects. The comparative analysis shows that the proposed method is robust and effective in the presence of various types of noise.

Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor.

Computer-aided detection (CAD) can help radiologists to detect pulmonary nodules at an early stage. In pulmonary nodule CAD systems, feature extraction is very important for describing the characteristics of nodule candidates. In this paper, we propose a novel three-dimensional shape-based feature descriptor to detect pulmonary nodules in CT scans. After lung volume segmentation, nodule candidates are detected using multi-scale dot enhancement filtering in the segmented lung volume. Next, we extract feature descriptors from the detected nodule candidates, and these are refined using an iterative wall elimination method. Finally, a support vector machine-based classifier is trained to classify nodules and non-nodules. The performance of the proposed system is evaluated on Lung Image Database Consortium data. The proposed method significantly reduces the number of false positives in nodule candidates. This method achieves 97.5% sensitivity, with only 6.76 false positives per scan.

Robust depth estimation and image fusion based on optimal area selection.

Mostly, 3D cameras having depth sensing capabilities employ active depth estimation techniques, such as stereo, the triangulation method or time-of-flight. However, these methods are expensive. The cost can be reduced by applying optical passive methods, as they are inexpensive and efficient. In this paper, we suggest the use of one of the passive optical methods named shape from focus (SFF) for 3D cameras. In the proposed scheme, first, an adaptive window is computed through an iterative process using a criterion. Then, the window is divided into four regions. In the next step, the best focused area among the four regions is selected based on variation in the data. The effectiveness of the proposed scheme is validated using image sequences of synthetic and real objects. Comparative analysis based on statistical metrics correlation, mean square error (MSE), universal image quality index (UIQI) and structural similarity (SSIM) shows the effectiveness of the proposed scheme.

Unorthodox approach toward microscopic shape from image focus using optical microscopy.

Shallow depth-of-field is an inherent property of optical microscope. Because of this limitation, it is usually impossible to image large three-dimensional (3D) objects entirely in focus. However, the in-focus information of the object's surface can be acquired over a range of images by optical sectioning of the object in consideration. These images can then be processed to generate a single in-focus image and further for 3D shape reconstruction using methods like Shape from focus (SFF). SFF represents a passive technique for recovering object shapes. Although numerous methods for SFF have been recently proposed, all follow similar precedent of focus measure application and depth recovery by maximizing the focus curves. As the conventional techniques assume the presence of prominent texture in the scene, the shape of weak textured surfaces are not recovered properly. In this manuscript, we have followed an unorthodox approach to recover shapes of microscopic objects using SFF. At first, the in-focus image is obtained, pursued by computing depth along the edges and their neighbors present in scene. Empty spaces in the final depth map are then calculated by surface interpolation. The proposed approach works well even for objects with weak textures.

Quantum and impulse noise filtering from breast mammogram images.

Recent advances in the field of image processing have shown that level of noise highly affect the quality and accuracy of classification when working with mammographic images. In this paper, we have proposed a method that consists of two major modules: noise detection and noise filtering. For detection purpose, neural network is used which effectively detect the noise from highly corrupted images. Pixel values of the window and some other features are used as feature for the training of neural network. For noise removal, three filters are used. The weighted average value of these three filters is filled on noisy pixels. The proposed technique has been tested on salt & pepper and quantum noise present in mammogram images. Peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) are used for comparison of proposed technique with different existing techniques. Experiments shows that proposed technique produce better results as compare to existing methods.

Searching surface orientation of microscopic objects for accurate 3D shape recovery.

In this article, we propose a new shape from focus (SFF) method to estimate 3D shape of microscopic objects using surface orientation cue of each object patch. Most of the SFF algorithms compute the focus value of a pixel from the information of neighboring pixels lying on the same image frame based on an assumption that the small object patch corresponding to the small neighborhood of a pixel is a plane parallel to the focal plane. However, this assumption fails in the optics with limited depth of field where the neighboring pixels of an image have different degree of focus. To overcome this problem, we try to search the surface orientation of the small object patch corresponding to each pixel in the image sequence. Searching of the surface orientation is done indirectly by principal component analysis. Then, the focus value of each pixel is computed from the neighboring pixels lying on the surface perpendicular to the corresponding surface orientation. Experimental results on synthetic and real microscopic objects show that the proposed method produces more accurate 3D shape in comparison to the existing techniques.

Morphological gradient based adapted selective filter for removal of Rician noise from magnetic resonance images.

Feature/edge-preserving noise removal techniques have a strong potential in several application domains including medical image processing. Magnetic resonance (MR) images have a tendency to gain Rician noise during acquisition. In this article, we have presented genetic algorithms based adapted selective non-local means (GASNLM) filter-based scheme for noise suppression of MR images while preserving the image features as much as possible. We have applied GASNLM filter with optimal parameter values for different frequency image regions to remove the noise. Filter parameter values are optimized by genetic algorithm (GA). A change in NLM filter known as selective weight matrix is also proposed to preserve the image features. The results prove soundness of the method. We have compared results with many well known and latest techniques, and the improvements are discussed.

Nonlinear approach for enhancement of image focus volume in shape from focus.

Mostly, shape-from-focus algorithms use local averaging using a fixed rectangle window to enhance the initial focus volume. In this linear filtering, the window size affects the accuracy of the depth map. A small window is unable to suppress the noise properly, whereas a large window oversmoothes the object shape. Moreover, the use of any window size smoothes focus values uniformly. Consequently, an erroneous depth map is obtained. In this paper, we suggest the use of iterative 3-D anisotropic nonlinear diffusion filtering (ANDF) to enhance the image focus volume. In contrast to linear filtering, ANDF utilizes the local structure of the focus values to suppress the noise while preserving edges. The proposed scheme is tested using image sequences of synthetic and real objects, and results have demonstrated its effectiveness.

Sampling for shape from focus in optical microscopy.

Shape from focus (SFF), which relies on image focus as a cue within sequenced images, represents a passive technique in recovering object shapes in scenes. Although numerous methods have been recently proposed, less attention has been paid to particular factors affecting them. In regard to SFF, one such critical factor impacting system application is the total number of images. A large data set requires a huge amount of computation power, whereas decreasing the number of images causes shape reconstruction to be crude and erroneous. The total number of images is inversely proportional to interframe distance or sampling step size. In this paper, interframe distance (or sampling step size) criteria for SFF systems have been formulated. In particular, light ray focusing is approximated by the use of a Gaussian beam followed by the formulation of a sampling expression using Nyquist sampling. Consequently, a fitting function for focus curves is also obtained. Experiments are performed on simulated and real objects to validate the proposed schemes.

MRT letter: segmentation and texture-based classification of breast mammogram images.

Breast cancer is the most common cancer diagnosed among women. In this article, support vector machine is used to classify digital mammogram images into malignant and benign. Wiener filter is used to handle the possible quantum noise, which is more likely to occur in mammograms. Stack-based connected component method is proposed for background removal, and the image is enhanced using retinax method. Seeded region growing algorithm is used to remove the pectoral muscle part of the mammogram. We have extracted 13 different multidomains' features for classification. Results show the superiority of the proposed algorithm in terms of sensitivity, specificity, and accuracy. We have used MIAS database of mammography for experimentation.

MRT letter: Optimal composite depth function for 3D shape recovery of microscopic objects.

Generally, in shape from focus techniques, a single focus measure is used in estimating the three-dimensional structure of microscopic objects. However, the performance of a single focus measure is limited to estimate accurately the depth map of diverse type of objects. To cope with this problem, we propose genetic programming based novel approach by developing an optimal composite depth (OCD) function for accurate depth estimation. This OCD function optimally combines the initial depth and focus information extracted from individual focus measures. An improved performance of this function is reported for synthetic and real world microscopic objects.

Depth from focus based on combinatorial optimization.

Depth from focus (DFF) is a technique to estimate the depth and 3D shape of an object from a sequence of images obtained at different focus settings. The DFF is presented as a combinatorial optimization problem. After the estimate of the initial depth map solution of an object, the algorithm updates the depth map iteratively from the specially defined neighborhood. The results of the proposed DFF algorithm have shown significant improvements in both the accuracy of the depth map estimation and the computational complexity, with respect to the existing DFF methods.