Automatic recognition of white blood cell images with memory efficient superpixel metric GNN: SMGNN.

An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based systems. In this paper, we presented a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consumes substantial computational resources and increases the risk of overfitting, especially when limited medical image samples are available for training. To address these challenges, we proposed a novel memory-efficient strategy that exploits graph structures derived from the images. Specifically, we introduced a lightweight superpixel-based graph neural network (GNN) and broke new ground by introducing superpixel metric learning to segment nucleus and cytoplasm. Remarkably, our proposed segmentation model superpixel metric graph neural network (SMGNN) achieved state of the art segmentation performance while utilizing at most 10000$ \times $ less than the parameters compared to existing approaches. The subsequent segmentation-based cell type classification processes showed satisfactory results that such automatic recognizing algorithms are accurate and efficient to execeute in hematological laboratories. Our code is publicly available at https://github.com/jyh6681/SPXL-GNN.

Combined measurement of serum zinc with PSA ameliorates prostate cancer screening efficiency via support vector machine algorithms.

Background: Early screening of prostate cancer (PCa) is pivotal but challenging in the clinical scenario due to the phenomena of false positivity or false negativity of some serological evaluations, e.g. PSA testing. Decline of serum Zn2+ levels in PCa patients reportedly plays a crucial role in early screening of PCa. Accordingly, we combined 4 indices comprising the serum levels of total PSA (tPSA), free PSA (fPSA), Zn2+ and demographic information (especially age) in order to ameliorate the efficacies of PCa screening with support vector machine (SVM) algorithms. Methods: A total of 858 male patients with prostate disorders and 345 healthy male controls were enrolled. Patients' data included 4 variables and serum Zn2+ was quantified via a self-invented Zn2+ responsive AIE-based fluorescent probe as previously published. tPSA and fPSA were routinely determined by a chemiluminescent method. Mathematical simulations were conducted to establish a SVM model for the combined diagnostics with the four variables. Moreover, ROC and its characteristic AUC were also employed to evaluate the classification efficacy of the model. Sigmoid function was utilized to estimate corresponding probabilities of classifying the clinical subjects as per 5 grades, which were incorporated into our established prostate index (PI) stratification system. Results: In SVM model, the mean AUC of the ROC with the quartet of variables was approximately 84% for PCa diagnosis, whereas the mean AUC of the ROCs with tPSA, fPSA, [Zn2+] or age alone was 64%, 62%, 55% and 59%, respectively. We further established an integrated prostate index (PI) stratification system with 5 grades and a software package to support clinicians in predicting PCa, with the accuracy of our risk stratification system being 83.3%, 91.6% and 83.3% in predicting normal, benign and PCa cases in corresponding groups. Follow-up findings especially MRI results and PI-RADS scores supported the reliability of this stratification platform as well. Conclusion: Findings from our present study demonstrated that index combination via SVM algorithms may well facilitate clinicians in early differential screening of PCa. Meanwhile, our established PI stratification system based on SVM model and Sigmoid function provided substantial accuracy in preclinical risk prediction of developing prostate cancer.

A Shortened Model for Logan Reference Plot Implemented via the Self-Supervised Neural Network for Parametric PET Imaging.

Dynamic PET imaging provides superior physiological information than conventional static PET imaging. However, the dynamic information is gained at the cost of a long scanning protocol; this limits the clinical application of dynamic PET imaging. We developed a modified Logan reference plot model to shorten the acquisition procedure in dynamic PET imaging by omitting the early-time information necessary for the conventional reference Logan model. The proposed model is accurate theoretically, but the straightforward approach raises the sampling problem in implementation and results in noisy parametric images. We then designed a self-supervised convolutional neural network to increase the noise performance of parametric imaging, with dynamic images of only a single subject for training. The proposed method was validated via simulated and real dynamic [Formula: see text]-fallypride PET data. Results showed that it accurately estimated the distribution volume ratio (DVR) in dynamic PET with a shortened scanning protocol, e.g., 20 minutes, where the estimations were comparable with those obtained from a standard dynamic PET study of 120 minutes of acquisition. Further comparisons illustrated that our method outperformed the shortened Logan model implemented with Gaussian filtering, regularization, BM4D and the 4D deep image prior methods in terms of the trade-off between bias and variance. Since the proposed method uses data acquired in a short period of time upon the equilibrium, it has the potential to add clinical values by providing both DVR and Standard Uptake Value (SUV) simultaneously. It thus promotes clinical applications of dynamic PET studies when neuronal receptor functions are studied.

The gestational age-specific difference in birthweight between singletons born after fresh and frozen embryo transfer: A cohort study.

INTRODUCTION: Accumulating studies have suggested singletons born after frozen embryo transfer (FET) were higher than those born after fresh embryo transfer (Fre-ET). However, fewer studies had investigated the gestational age-specific between-group difference in birthweight. This study aimed to investigate the gestational week-specific difference in singleton birthweight after FET vs Fre-ET and explore potential factors that impact the difference. MATERIAL AND METHODS: In this retrospective cohort study, a total of 25 863 singletons were included. Multivariable linear regression and logistic regression were used to evaluate the between-group differences in mean birthweight and the incidences of large for gestational age (LGA) and small for gestational age (SGA), respectively. RESULTS: Multivariable regression analyses showed a statistically significant interaction between types of embryo transfer (ie FET vs Fre-ET) and the gestational week on mean birthweight (P < 0.001) and on the risks of large for gestational age (P = 0.001) and small for gestational age (P < 0.001). When stratified by gestational week, the differences in mean birthweight and the risks of LGA and SGA were only observed in singletons born at 37 gestational weeks or later. After adjusting for confounders, full-term but not preterm singletons born after FET had a higher birthweight (3497.58 ± 439.73 g vs 3445.67 ± 450.24 g; adjusted mean difference 58.35 g; 95% confidence interval [CI] 38.72-77.98 g), a higher risk of LGA (24.3% vs 21.1%; adjusted odds ratio [OR] 1.28, 95% CI 1.15-1.42) and a lower risk of SGA (3.1% vs 4.8%; adjusted OR 0.61, 95% CI 0.53-0.70) compared with those born after Fre-ET. CONCLUSIONS: The differences in birthweight between FET and Fre-ET were observed in full-term singletons but not preterm singletons.

ANGPTL4 inhibits granulosa cell proliferation in polycystic ovary syndrome by EGFR/JAK1/STAT3-mediated induction of p21.

Polycystic ovary syndrome (PCOS) is one of the most common, heterogenous endocrine disorders and is the leading cause of ovulatory obstacle associated with abnormal folliculogenesis. Dysfunction of ovarian granulosa cells (GCs) is recognized as a major factor that underlies abnormal follicle maturation. Angiopoietin-like 4 (ANGPTL4) expression in GCs differs between patients with and without PCOS. However, the role and mechanism of ANGPTL4 in impaired follicular development are still poorly understood. Here, the case-control study was designed to investigate the predictive value of ANGPTL4 in PCOS while cell experiments in vitro were set for mechanism research. Results found that ANGPTL4 levels in serum and in follicular fluid, and its expression in GCs, were upregulated in patients with PCOS. In KGN and SVOG cells, upregulation of ANGPTL4 inhibited the proliferation of GCs by blocking G1/S cell cycle progression, as well as the molecular activation of the EGFR/JAK1/STAT3 cascade. Moreover, the STAT3-dependent CDKN1A(p21) promoter increased CDKN1A transcription, resulting in remarkable suppression effect on GCs. Together, our results demonstrated that overexpression of ANGPTL4 inhibited the proliferation of GCs through EGFR/JAK1/STAT3-mediated induction of p21, thus providing a novel epigenetic mechanism for the pathogenesis of PCOS.

SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data.

This work aims to improve limited-angle (LA) cone beam computed tomography (CBCT) by developing deep learning (DL) methods for real clinical CBCT projection data, which is the first feasibility study of clinical-projection-data-based LA-CBCT, to the best of our knowledge. In radiation therapy (RT), CBCT is routinely used as the on-board imaging modality for patient setup. Compared to diagnostic CT, CBCT has a long acquisition time, e.g., 60 seconds for a full 360° rotation, which is subject to the motion artifact. Therefore, the LA-CBCT, if achievable, is of the great interest for the purpose of RT, for its proportionally reduced scanning time in addition to the radiation dose. However, LA-CBCT suffers from severe wedge artifacts and image distortions. Targeting at real clinical projection data, we have explored various DL methods such as image/data/hybrid-domain methods and finally developed a so-called Structure-Enhanced Attention Network (SEA-Net) method that has the best image quality from clinical projection data among the DL methods we have implemented. Specifically, the proposed SEA-Net employs a specialized structure enhancement sub-network to promote texture preservation. Based on the observation that the distribution of wedge artifacts in reconstruction images is non-uniform, the spatial attention module is utilized to emphasize the relevant regions while ignores the irrelevant ones, which leads to more accurate texture restoration.

Proteomics and bioinformatics analysis of follicular fluid from patients with polycystic ovary syndrome.

Objectives: Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder with heterogeneous manifestations and complex etiology. We used quantitative proteomics analysis based on mass spectrometry to identify the differences in proteomics profiles for follicular fluid obtained from patients with or without PCOS and explore possible mechanisms underlying PCOS. Methods: Follicular fluid samples were collected from infertile patients with (n = 9) or without (n = 9) PCOS. Total protein was extracted, quantitatively labeled with a tandem mass tag (TMT), and analyzed using liquid chromatography-mass spectrometry (LC-MS). TMT-based proteomics and bioinformatics analysis were used to determine the differentially expressed proteins (DEPs) and understand the protein networks. The analysis included protein annotation, unsupervised hierarchical clustering, functional classification, functional enrichment and clustering, and protein-protein interaction analysis. Selected DEPs were confirmed by ELISA, and correlation analysis was performed between these DEPs and the clinical characteristics. Results: In this study, we have identified 1,216 proteins, including 70 DEPs (32 upregulated proteins, 38 downregulated proteins). Bioinformatics analysis revealed that the inflammatory response, complement and coagulation cascades, activation of the immune response, lipid transport, and regulation of protein metabolic processes were co-enriched in patients with PCOS. Based on ELISA results, insulin-like growth factor binding protein 1 (IGFBP1) and apolipoprotein C2 (APOC2) were differentially expressed between patients with and without PCOS. Follicular IGFBP1 showed a positive correlation with the serum levels of high-density lipoprotein cholesterol (HDL-C) (r = 0.3046, p = 0.0419), but negatively correlated with the serum levels of anti-Müllerian hormone (AMH) (r = -0.2924, p = 0.0354) and triglycerides (r = -0.3177, p = 0.0246). Follicular APOC2 was negatively correlated with the serum apolipoprotein A1 (APOA1) levels (r = 0.4509, p = 0.0002). Conclusion: Our study identified DEPs in the follicular fluid of patients with PCOS. Inflammatory response, complement and coagulation cascades, activation of the immune response, lipid transport, and regulation of protein metabolic process were deregulated in PCOS, which may play essential roles in the pathogenesis of PCOS.

The sex-specific difference in singleton birth weight after frozen embryo transfer compared with fresh embryo transfer: a secondary analysis of 3 randomized trials.

OBJECTIVE: To assess whether the between-group difference in singleton birth weight following frozen vs. fresh embryo transfer varied with infant sex. DESIGN: A post hoc exploratory secondary analysis of data from three multicenter randomized trials compared the live birth rates between freeze-only vs. fresh embryo transfer. SETTING: Academic fertility centers. PATIENT(S): A total of 1,886 women who achieved singleton live birth after a frozen or fresh embryo transfer during these trials were included. INTERVENTION(S): Women underwent either a frozen or fresh embryo transfer. MAIN OUTCOME MEASURE(S): Mean birth weight, large for gestational age (LGA), and small for gestational age (SGA). RESULT(S): There was an interaction between the types of embryo transfer and infant sex on the birth weight and on the incidences of LGA and SGA. Among male infants, compared with singletons following fresh embryo transfer, singletons following frozen embryo transfer had higher mean birth weights (3,520.6 ± 526.1 vs. 3,345.1 ± 524.9 g), a higher risk of being LGA (25.2% vs. 15.7%), and a lower risk of being SGA (3.3% vs. 6.1%). However, among the female infants, no statistically significant difference was found in the mean birth weight (3,336.5 ± 514.8 vs. 3,299.5 ± 485.0 g) or the risks of being LGA (18.8% vs. 15.7%) or SGA (5.2% vs. 6.0%) between frozen and fresh embryo transfer. CONCLUSION(S): Male singletons born after frozen embryo transfer were more likely to have a higher birth weight than those born after fresh embryo transfer.

AirNet: Fused analytical and iterative reconstruction with deep neural network regularization for sparse-data CT.

PURPOSE: Sparse-data computed tomography (CT) frequently occurs, such as breast tomosynthesis, C-arm CT, on-board four-dimensional cone-beam CT (4D CBCT), and industrial CT. However, sparse-data image reconstruction remains challenging due to highly undersampled data. This work develops a data-driven image reconstruction method for sparse-data CT using deep neural networks (DNN). METHODS: The new method so-called AirNet is designed to incorporate the benefits from analytical reconstruction method (AR), iterative reconstruction method (IR), and DNN. It is built upon fused analytical and iterative reconstruction (AIR) that synergizes AR and IR via the optimization framework of modified proximal forward-backward splitting (PFBS). By unrolling PFBS into IR updates of CT data fidelity and DNN regularization with residual learning, AirNet utilizes AR such as FBP during the data fidelity, introduces dense connectivity into DNN regularization, and learns PFBS coefficients and DNN parameters that minimize the loss function during the training stage; and then AirNet with trained parameters can be used for end-to-end image reconstruction. RESULTS: A CT atlas of 100 prostate scans was used to validate the AirNet in comparison with state-of-art DNN-based postprocessing and image reconstruction methods. The validation loss in AirNet had the fastest decreasing rate, owing to inherited fast convergence from AIR. AirNet was robust to noise in projection data and content differences between the training set and the images to be reconstructed. The impact of image quality on radiotherapy treatment planning was evaluated for both photon and proton therapy, and AirNet achieved the best treatment plan quality, especially for proton therapy. For example, with limited-angle data, the maximal target dose for AirNet was 109.5% in comparison with the ground truth 109.1%, while it was significantly elevated to 115.1% and 128.1% for FBPConvNet and LEARN, respectively. CONCLUSIONS: A new image reconstruction AirNet is developed for sparse-data CT image reconstruction. AirNet achieved the best image reconstruction quality both visually and quantitatively among all methods under comparison for all sparse-data scenarios (sparse-view and limited-angle), and provided the best photon and proton treatment plan quality based on sparse-data CT.

Low-dose CT with deep learning regularization via proximal forward-backward splitting.

Low-dose x-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops new image reconstruction methods with deep learning (DL) regularization for LDCT. Our methods are based on the unrolling of a proximal forward-backward splitting (PFBS) framework with data-driven image regularization via deep neural networks. In contrast to PFBS-IR, which utilizes standard data fidelity updates via an iterative reconstruction (IR) method, PFBS-AIR involves preconditioned data fidelity updates that fuse the analytical reconstruction (AR) and IR methods in a synergistic way, i.e. fused analytical and iterative reconstruction (AIR). The results suggest that the DL-regularized methods (PFBS-IR and PFBS-AIR) provide better reconstruction quality compared to conventional methods (AR or IR). In addition, owing to the AIR, PFBS-AIR noticeably outperformed PFBS-IR and another DL-based postprocessing method, FBPConvNet.

Image-domain multimaterial decomposition for dual-energy CT based on prior information of material images.

PURPOSE: Dual-Energy Computed Tomography (DECT) is of great interest in medical imaging, security inspection, and nondestructive testing. Most DECT reconstruction methods focus on producing two material images with different linear attenuation coefficients. However, the ability to reconstruct three or more basis materials is clinically and industrially important. Under the assumption that there are at most three materials in each pixel, there are a few methods that estimate multiple material images from DECT measurements by enforcing sum-to-one and a box constraint ([0 1]) derived from both the volume and mass conservation assumption. The recently proposed image-domain multimaterial decomposition (MMD) method introduces edge-preserving regularization for each material image. It enforces the assumption that there are at most three materials in each pixel using a time-consuming loop over all possible material triplets. However, this method neglects relations among material images. We propose a new image-domain MMD model for DECT that considers the prior information that different material images have common or complementary edges and encourages sparsity of material composition in each pixel using regularization. METHOD: The proposed PWLS-TNV-ℓ0 method uses penalized weighted least-square (PWLS) reconstruction with three regularization terms. The first term is total nuclear variation (TNV) that accounts for the image property that basis material images share common or complementary boundaries and each material image is piecewise constant. The second term is an ℓ0 norm that encourages each pixel containing a small subset of material types out of several possible materials. The third term is a characteristic function based on sum-to-one and a box constraint derived from the volume and mass conservation assumption. We apply the Alternating Direction Method of Multipliers (ADMM) to optimize the cost function of the PWLS-TNV-ℓ0 method. RESULT: We evaluated the proposed method on a simulated digital phantom, Catphan©600 phantom and patient's pelvis data. We implemented two existing image-domain MMD methods for DECT, the Direct Inversion and the PWLS-EP-LOOP method. We initialized the PWLS-TNV-ℓ0 method and the PWLS-EP-LOOP method with the results of the Direct Inversion method and compared performance of the proposed method with that of the PWLS-EP-LOOP method. The proposed method lowers the bias of decomposed material fractions by 84.47% in the digital phantom study, by 99.50% in the Catphan©600 phantom study, and by 99.64% in the pelvis patient study, respectively, compared to the PWLS-EP-LOOP method. The proposed method reduces noise standard deviation (STD) by 52.21% in the Catphan©600 phantom study, and by 16.74% in the patient's pelvis study, compared to the PWLS-EP-LOOP method. The proposed method increases volume fraction accuracy by 6.04%,20.55%, and 13.46% for the digital phantom, the Catphan©600 phantom, and the patient's pelvis study, respectively, compared to the PWLS-EP-LOOP method. Compared with the PWLS-EP-LOOP method, the root mean square percentage error [RMSE(%)] of electron densities in the Catphan©600 phantom is decreased by about 7.39%. CONCLUSIONS: We proposed an image-domain MMD method, PWLS-TNV-ℓ0 , for DECT. The PWLS-TNV-ℓ0 method takes low rank property of material image gradients, sparsity of material composition and mass and volume conservation into consideration. The proposed method suppresses noise, reduces cross contamination, and improves accuracy in the decomposed material images, compared to the PWLS-EP-LOOP method.