Thinking Twice: Clinical-Inspired Thyroid Ultrasound Lesion Detection Based on Feature Feedback

Accurate detection of thyroid lesions is a critical aspect of computer-aided diagnosis. However, most existing detection methods perform only one feature extraction process and then fuse multi-scale features, which can be affected by noise and blurred features in ultrasound images. In this study, we propose a novel detection network based on a feature feedback mechanism inspired by clinical diagnosis. The mechanism involves first roughly observing the overall picture and then focusing on the details of interest. It comprises two parts: a feedback feature selection module and a feature feedback pyramid. The feedback feature selection module efficiently selects the features extracted in the first phase in both space and channel dimensions to generate high semantic prior knowledge, which is similar to coarse observation. The feature feedback pyramid then uses this high semantic prior knowledge to enhance feature extraction in the second phase and adaptively fuses the two features, similar to fine observation. Additionally, since radiologists often focus on the shape and size of lesions for diagnosis, we propose an adaptive detection head strategy to aggregate multi-scale features. Our proposed method achieves an AP of 70.3% and AP50 of 99.0% on the thyroid ultrasound dataset and meets the real-time requirement. The code is available at https://github.com/HIT-wanglingtao/Thinking-Twice.Comment: 20 pages, 11 figures, released code for https://github.com/HIT-wanglingtao/Thinking-Twic

CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network

U-Net and its extensions have achieved great success in medical image segmentation. However, due to the inherent local characteristics of ordinary convolution operations, U-Net encoder cannot effectively extract global context information. In addition, simple skip connections cannot capture salient features. In this work, we propose a fully convolutional segmentation network (CMU-Net) which incorporates hybrid convolutions and multi-scale attention gate. The ConvMixer module extracts global context information by mixing features at distant spatial locations. Moreover, the multi-scale attention gate emphasizes valuable features and achieves efficient skip connections. We evaluate the proposed method using both breast ultrasound datasets and a thyroid ultrasound image dataset; and CMU-Net achieves average Intersection over Union (IoU) values of 73.27% and 84.75%, and F1 scores of 84.81% and 91.71%. The code is available at https://github.com/FengheTan9/CMU-Net.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion

The U-shaped architecture has emerged as a crucial paradigm in the design of medical image segmentation networks. However, due to the inherent local limitations of convolution, a fully convolutional segmentation network with U-shaped architecture struggles to effectively extract global context information, which is vital for the precise localization of lesions. While hybrid architectures combining CNNs and Transformers can address these issues, their application in real medical scenarios is limited due to the computational resource constraints imposed by the environment and edge devices. In addition, the convolutional inductive bias in lightweight networks adeptly fits the scarce medical data, which is lacking in the Transformer based network. In order to extract global context information while taking advantage of the inductive bias, we propose CMUNeXt, an efficient fully convolutional lightweight medical image segmentation network, which enables fast and accurate auxiliary diagnosis in real scene scenarios. CMUNeXt leverages large kernel and inverted bottleneck design to thoroughly mix distant spatial and location information, efficiently extracting global context information. We also introduce the Skip-Fusion block, designed to enable smooth skip-connections and ensure ample feature fusion. Experimental results on multiple medical image datasets demonstrate that CMUNeXt outperforms existing heavyweight and lightweight medical image segmentation networks in terms of segmentation performance, while offering a faster inference speed, lighter weights, and a reduced computational cost. The code is available at https://github.com/FengheTan9/CMUNeXt.Comment: 8 pages, 3 figure

Mass Spectrometric Sampling of a Liquid Surface by Nanoliter Droplet Generation from Bursting Bubbles and Focused Acoustic Pulses: Application to Studies of Interfacial Chemistry

The complex chemistry occurring at the interface between liquid and vapor phases contributes significantly to the dynamics and evolution of numerous chemical systems of interest, ranging from damage to the human lung surfactant layer to the aging of atmospheric aerosols. This work presents two methodologies to eject droplets from a liquid water surface and analyze them via mass spectrometry. In bursting bubble ionization (BBI), droplet ejection is achieved via the formation of a jet following bubble rupture at the surface of a liquid to yield 250 μm diameter droplets (10 nL volume). In interfacial sampling by an acoustic transducer (ISAT), droplets are produced by focusing pulsed piezoelectric transducer-generated acoustic waves at the surface of a liquid, resulting in the ejection of droplets of 100 μm in diameter (500 pL volume). In both experimental methodologies, ejected droplets are aspirated into the inlet of the mass spectrometer, resulting in the facile formation of gas-phase ions. We demonstrate the ability of this technique to readily generate spectra of surface-active analytes, and we compare the spectra to those obtained by electrospray ionization. Charge measurements indicate that the ejected droplets are near-neutral (<0.1% of the Rayleigh limit), suggesting that gas-phase ion generation occurs in the heated transfer capillary of the instrument in a mechanism similar to thermospray or sonic spray ionization. Finally, we present the oxidation of oleic acid by ozone as an initial demonstration of the ability of ISAT-MS to monitor heterogeneous chemistry occurring at a planar water/air interface

Structural and functional abnormities of amygdala and prefrontal cortex in major depressive disorder with suicide attempts

Finding neural features of suicide attempts (SA) in major depressive disorder (MDD) may be helpful in preventing suicidal behavior. The ventral and medial prefrontal cortex (PFC), as well as the amygdala form a circuit implicated in emotion regulation and the pathogenesis of MDD. The aim of this study was to identify whether patients with MDD who had a history of SA show structural and functional connectivity abnormalities in the amygdala and PFC relative to MDD patients without a history of SA. We measured gray matter volume in the amygdala and PFC and amygdala-PFC functional connectivity using structural and functional magnetic resonance imaging (MRI) in 158 participants [38 MDD patients with a history of SA, 60 MDD patients without a history of SA, and 60 healthy control (HC)]. MDD patients with a history of SA had decreased gray matter volume in the right and left amygdala (F = 30.270, P = 0.000), ventral/medial/dorsal PFC (F = 15.349, P = 0.000), and diminished functional connectivity between the bilateral amygdala and ventral and medial PFC regions (F = 22.467, P = 0.000), compared with individuals who had MDD without a history of SA, and the HC group. These findings provide evidence that the amygdala and PFC may be closely related to the pathogenesis of suicidal behavior in MDD and implicate the amygdala-ventral/medial PFC circuit as a potential target for suicide intervention

Structural and functional abnormities of amygdala and prefrontal cortex in major depressive disorder with suicide attempts

Finding neural features of suicide attempts (SA) in major depressive disorder (MDD) may be helpful in preventing suicidal behavior. The ventral and medial prefrontal cortex (PFC), as well as the amygdala form a circuit implicated in emotion regulation and the pathogenesis of MDD. The aim of this study was to identify whether patients with MDD who had a history of SA show structural and functional connectivity abnormalities in the amygdala and PFC relative to MDD patients without a history of SA. We measured gray matter volume in the amygdala and PFC and amygdala-PFC functional connectivity using structural and functional magnetic resonance imaging (MRI) in 158 participants [38 MDD patients with a history of SA, 60 MDD patients without a history of SA, and 60 healthy control (HC)]. MDD patients with a history of SA had decreased gray matter volume in the right and left amygdala (F = 30.270, P = 0.000), ventral/medial/dorsal PFC (F = 15.349, P = 0.000), and diminished functional connectivity between the bilateral amygdala and ventral and medial PFC regions (F = 22.467, P = 0.000), compared with individuals who had MDD without a history of SA, and the HC group. These findings provide evidence that the amygdala and PFC may be closely related to the pathogenesis of suicidal behavior in MDD and implicate the amygdala-ventral/medial PFC circuit as a potential target for suicide intervention

Influence of external heat sources on volumetric thermal errors of precision machine tools

Volumetric accuracy is susceptible to thermal gradient caused by internal heat source (IHS) and external heat source (EHS). A temperature-structure multi-step calculation method is presented to investigate the influences of EHSs on volumetric thermal errors of precision machine tools. The temperature and structure of the machine tool are simulated first, and then, the volumetric thermal errors are calculated using multi-body theory method. Simulations are completed to study the effects of different EHSs on a machine tool, and series of validating experiments are carried out to verify the modeling method. The test results in specific position and working condition revealed that EHSs contribute 53, 21, and 68% of thermal deviations in X, Y, and Z directions individually. It is illustrated that the EHS is an important factor affecting the volumetric accuracy of precision machine tools. The methods provided in this paper are valuable for machine tool designers to evaluate the EHS effects on volumetric thermal errors during designing process; furthermore, some insulating measures are suggested to improve the accuracy and accuracy stability of precision machine tools by reducing the EHS influences

Application of the Empirical Bayes Method with the Finite Mixture Model for Identifying Accident-Prone Spots

Hotspot identification (HSID) is an important component of the highway safety management process. A number of methods have been proposed to identify hotspots. Among these methods, previous studies have indicated that the empirical Bayes (EB) method can outperform other methods for identifying hotspots, since the EB method combines the historical crash records of the site and expected number of crashes obtained from a safety performance function (SPF) for similar sites. However, the SPFs are usually developed based on a large number of sites, which may contain heterogeneity in traffic characteristic. As a result, the hotspot identification accuracy of EB methods can possibly be affected by SPFs, when heterogeneity is present in crash data. Thus, it is necessary to consider the heterogeneity and homogeneity of roadway segments when using EB methods. To address this problem, this paper proposed three different classification-based EB methods to identify hotspots. Rural highway crash data collected in Texas were analyzed and classified into different groups using the proposed methods. Based on the modeling results for Texas crash dataset, it is found that one proposed classification-based EB method performs better than the standard EB method as well as other HSID methods

Surface water numerical modelling for the Clarence-Moreton bioregion. Product 2.6.1 from the Clarence-Moreton Bioregional Assessment

No abstract available

Surface water numerical modelling for the Clarence-Moreton bioregion. Product 2.6.1 from the Clarence-Moreton Bioregional Assessment

No abstract available