3D bi-directional transformer U-Net for medical image segmentation

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics

Analysis of electrophysiological activation of the uterus during human labor contractions

This cohort study uses electromyometrial imaging to examine the underlying electrophysiological origins of human labor at the myometrium level

Magnetic resonance imaging of the supra-cervical fetal membrane detects an increased risk of prelabor rupture of membranes

OBJECTIVE: In 10% of term deliveries and 40% of preterm deliveries, the fetal membrane (FM) ruptures before labor. However, the ability to predict these cases of premature rupture of membranes (PROM) and preterm premature rupture of membranes (PPROM) is very limited. In this paper, our objective was to determine whether a prediction method based on T2 weighted magnetic resonance imaging (MRI) of the supra-cervical FM could predict PROM and PPROM. METHODS: This prospective cohort study enrolled 77 women between the 28th and 37th weeks of gestation. Two indicators of fetal membrane defects, including prolapsed depth \u3e5 mm and signal abnormalities, are investigated for our prediction. Fisher\u27s exact test was used to determine whether prolapsed depth \u3e5 mm and/or signal abnormalities were associated with PROM and PPROM. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for prolapsed depth \u3e5 mm, signal abnormalities, and the combination of prolapsed depth \u3e5 mm and signal abnormalities. RESULT: Among 12 women with PROM (5 preterm and 7 term, prior to labor onset), 9 had membrane prolapse \u3e5 mm and 5 had FM signal abnormalities. Among 65 women with rupture of membranes at term, 2 had membrane prolapse \u3e5 mm and 1 had signal abnormalities. By Fisher\u27s exact test both indicators, membrane prolapse \u3e5 mm and signal abnormalities, were associated with PROM (P\u3c0.001, P\u3c0.001) and PPROM (P=0.001, P\u3c0.001). Additionally, membrane prolapse \u3e5 mm, signal abnormalities, and the combination of the two indicators all demonstrated high specificity for predicting PROM (96.9%, 98.5%, and 100%, respectively) and PPROM (90.3%, 97.2%, and 100%, respectively). CONCLUSION: MRI can distinguish the supra-cervical fetal membran

In vivo assessment of supra-cervical fetal membrane by MRI 3D CISS: A preliminary study

In approximately 8% of term births and 33% of pre-term births, the fetal membrane (FM) ruptures before delivery

Noninvasive electromyometrial imaging of human uterine maturation during term labor

Electromyometrial imaging (EMMI) was recently developed to image the three-dimensional (3D) uterine electrical activation during contractions noninvasively and accurately in sheep. Herein we describe the development and application of a human EMMI system to image and evaluate 3D uterine electrical activation patterns at high spatial and temporal resolution during human term labor. We demonstrate the successful integration of the human EMMI system during subjects\u27 clinical visits to generate noninvasively the uterine surface electrical potential maps, electrograms, and activation sequence through an inverse solution using up to 192 electrodes distributed around the abdomen surface. Quantitative indices, including the uterine activation curve, are developed and defined to characterize uterine surface contraction patterns. We thus show that the human EMMI system can provide detailed 3D images and quantification of uterine contractions as well as novel insights into the role of human uterine maturation during labor progression

3D bi-directional transformer U-Net for medical image segmentation

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics

Baseline microglial activation correlates with brain amyloidosis and longitudinal cognitive decline in Alzheimer disease

BACKGROUND AND OBJECTIVES: This study aims to quantify microglial activation in individuals with Alzheimer disease (AD) using the 18-kDa translocator protein (TSPO) PET imaging in the hippocampus and precuneus, the 2 AD-vulnerable regions, and to evaluate the association of baseline neuroinflammation with amyloidosis, tau, and longitudinal cognitive decline. METHODS: Twenty-four participants from the Knight Alzheimer Disease Research Center (Knight ADRC) were enrolled and classified into stable cognitively normal, progressor, and symptomatic AD groups based on clinical dementia rating (CDR) at 2 or more clinical assessments. The baseline TSPO radiotracer [11C]PK11195 was used to image microglial activation. Baseline CSF concentrations of Aβ42, Aβ42/Aβ40 ratio, tau phosphorylated at position 181 (p-tau181), and total tau (t-tau) were measured. Clinical and cognitive decline were examined with longitudinal CDR and cognitive composite scores (Global and Knight ADRC-Preclinical Alzheimer Cognitive Composite [Knight ADRC-PACC] Score). RESULTS: Participants in the progressor and symptomatic AD groups had significantly elevated [11C]PK11195 standard uptake value ratios (SUVRs) in the hippocampus but not in the precuneus region. In the subcohort with CSF biomarkers (16 of the 24), significant negative correlations between CSF Aβ42 or Aβ42/Aβ40 and [11C]PK11195 SUVR were observed in the hippocampus and precuneus. No correlations were observed between [11C]PK11195 SUVR and CSF p-tau181 or t-tau at baseline in those regions. Higher baseline [11C]PK11195 SUVR averaged in the whole cortical regions predicted longitudinal decline on cognitive tests. DISCUSSION: Microglial activation is increased in individuals with brain amyloidosis and predicts worsening cognition in AD. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that in patients with AD, higher baseline [11C]PK11195 SUVR averaged in the whole cortical regions was associated with longitudinal decline on cognitive tests

Advanced Diffusion MRI Technique and Applications in Placenta and Brain

Among a series of contrasts of MRI, diffusion MRI (dMRI) is different from conventional MRI by its ability to capture information about the random movement of water molecules and their interaction with local tissue microstructures. This capability holds the potential to reveal intricate subvoxel microstructural information linked to pathological changes, offering potential imaging biomarkers. A higher-order diffusion analysis model helps to yield more accurate and specific parameters but requires more extensive parameter tuning and higher image quality. In this study, we firstly fine-tuned the previously developed diffusion basis spectrum imaging (DBSI) method to strike a balance between efficiency and accuracy when dealing with various b-table designs, signal-to-noise ratios (SNR), and organ-of-interest (Chapter 2). The dMRI data, usually obtained through echoplanar imaging (EPI), frequently encounter challenges related to misalignment of the image caused by the motion of the subject. This is- sue is particularly pronounced in placenta imaging, where fetal movements are unpredictable and uncontrollable. To address this issue, we devised a tailored registration pipeline capable of mitigating both intra-volume and inter-volume misalignment (Chapter 3). Furthermore, we noticed the absence of detailed segmentation methods within placenta re- gion, as most of the previous study used the average value from either the entire placenta or several manually labelled regions. We developed an automatic separation method con- sidering dMRI and T2* MRI features that divides placenta into subregions. The improved more specific compartment-wise quantification revealed new insights in longitudinal changes in placenta (Chapter 4). In the DBSI placenta application, we carefully performed ex vivo validation and simulation validation before in-vivo application. The method imaged the spatial distribution of the placental immune cells and revealed significantly greater immune cell infiltration in the in- flammation placentas throughout gestation, demonstrating its potential to serve as a clinical tool to monitor the placenta immune status of pregnancy longitudinally without ionizing radiation (Chapter 5). Finally, although DBSI was initially developed and validated in brain studies, we recently recognized its potential for enhancement, inspired by an in vivo observation of increasing cell ADC along progress of Alzheimer’s disease (AD). We targeted microglia as a potential marker and hypothesized that DBSI can quantify the proliferation and activation of microglia with Monte-Carlo simulated reference signal from real microglia model (Chapter 6)

The protective effects of Lipoxin A(4) during the early phase of severe acute pancreatitis in rats

Objective. Our aim was to investigate the protective effects of a Lipoxin A(4) analogue (LXA(4)) in the early phase of acute pancreatitis in rats. Materials and methods. Severe acute pancreatitis (SAP) was induced by injection of 5% sodium taurocholate into the pancreatic duct. Rats with SAP were treated with LXA(4) (0.1 mg/kg), 10 min after the 5% sodium taurocholate injection, after which LXA(4) was administrated every 8 hours, three times (LXA(4) group). The sham group was only given the vehicle after operation. Plasma amylase activity, serum levels of interleukin-1 (IL-1), IL-6, and tumor necrosis factor-alpha (TNF-alpha) were measured at 4, 12, and 24 h after induction of SAP. The pancreatic index and histopathologic observations were evaluated and the expression of intercellular adhesion molecule-1 (ICAM-1) and NF-kappa B p65 in the pancreas, and the expression of ICAM-1 in the lungs were detected by immunohistochemistry. Results. LXA(4) treated rats had lower serum levels of TNF-alpha, IL-1, and IL-6 at all time points measured (p < 0.05), but significantly differed in plasma amylase activity only at 24 h as compared with the SAP group. The pancreatic index and the scores of pancreatitic histopathologic evaluations were lower in the LXA(4) group as compared to the SAP group. Immunohistochemistry showed that LXA(4) attenuated the expression of ICAM-1 and NF-kappa B p65 in the pancreas, as well as the expression of ICAM-1 in the lungs in animals with pancreatitis (p < 0.05). Conclusions. We demonstrate that LXA(4) has protective effects in experimental SAP, which may be achieved by inhibiting the NF-kappa B signalling pathway, thereby reducing the production of proinflammatory cytokines