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

TFPI-2 is a putative tumor suppressor gene frequently inactivated by promoter hypermethylation in nasopharyngeal carcinoma

<p>Abstract</p> <p>Background</p> <p>Epigenetic silencing of tumor suppressor genes play important roles in NPC tumorgenesis. Tissue factor pathway inhibitor-2 (TFPI-2), is a protease inhibitor. Recently, <it>TFPI-2 </it>was suggested to be a tumor suppressor gene involved in tumorigenesis and metastasis in some cancers. In this study, we investigated whether <it>TFPI-2 </it>was inactivated epigenetically in nasopharyngeal carcinoma (NPC).</p> <p>Methods</p> <p>Transcriptional expression levels of <it>TFPI-2 </it>was evaluated by RT-PCR. Methylation status were investigated by methylation specific PCR and bisulfate genomic sequencing. The role of <it>TFPI-2 </it>as a tumor suppressor gene in NPC was addressed by re-introducing <it>TFPI-2 </it>expression into the NPC cell line CNE2.</p> <p>Results</p> <p><it>TFPI-2 </it>mRNA transcription was inactivated in NPC cell lines. <it>TFPI-2 </it>was aberrantly methylated in 66.7% (4/6) NPC cell lines and 88.6% (62/70) of NPC primary tumors, but not in normal nasopharyngeal epithelia. <it>TFPI-2 </it>expression could be restored in NPC cells after demethylation treatment. Ectopic expression of TFPI-2 in NPC cells induced apoptosis and inhibited cell proliferation, colony formation and cell migration.</p> <p>Conclusions</p> <p>Epigenetic inactivation of <it>TFPI-2 </it>by promoter hypermethylation is a frequent and tumor specific event in NPC. <it>TFPI-2 </it>might be considering as a putative tumor suppressor gene in NPC.</p

Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model

Medical image segmentation is a critical step in computer-aided diagnosis, and convolutional neural networks are popular segmentation networks nowadays. However, the inherent local operation characteristics make it difficult to focus on the global contextual information of lesions with different positions, shapes, and sizes. Semi-supervised learning can be used to learn from both labeled and unlabeled samples, alleviating the burden of manual labeling. However, obtaining a large number of unlabeled images in medical scenarios remains challenging. To address these issues, we propose a Multi-level Global Context Cross-consistency (MGCC) framework that uses images generated by a Latent Diffusion Model (LDM) as unlabeled images for semi-supervised learning. The framework involves of two stages. In the first stage, a LDM is used to generate synthetic medical images, which reduces the workload of data annotation and addresses privacy concerns associated with collecting medical data. In the second stage, varying levels of global context noise perturbation are added to the input of the auxiliary decoder, and output consistency is maintained between decoders to improve the representation ability. Experiments conducted on open-source breast ultrasound and private thyroid ultrasound datasets demonstrate the effectiveness of our framework in bridging the probability distribution and the semantic representation of the medical image. Our approach enables the effective transfer of probability distribution knowledge to the segmentation network, resulting in improved segmentation accuracy. The code is available at https://github.com/FengheTan9/Multi-Level-Global-Context-Cross-Consistency.Comment: 10 pages, 8 figures, Released code for https://github.com/FengheTan9/Multi-Level-Global-Context-Cross-Consistenc

Antimicrobial Resistance Profile and Genotypic Characteristics of Streptococcus suis Capsular Type 2 Isolated from Clinical Carrier Sows and Diseased Pigs in China

Streptococcus suis serotype 2 is an important zoonotic pathogen. Antimicrobial resistance phenotypes and genotypic characterizations of S. suis 2 from carrier sows and diseased pigs remain largely unknown. In this study, 96 swine S. suis type 2, 62 from healthy sows and 34 from diseased pigs, were analyzed. High frequency of tetracycline resistance was observed, followed by sulfonamides. The lowest resistance of S. suis 2 for -lactams supports their use as the primary antibiotics to treat the infection of serotype 2. In contrast, 35 of 37 S. suis 2 with MLS B phenotypes were isolated from healthy sows, mostly encoded by the ermB and/or the mefA genes. Significantly lower frequency of mrp+/epf +/sly+ was observed among serotype 2 from healthy sows compared to those from diseased pigs. Furthermore, isolates from diseased pigs showed more homogeneously genetic patterns, with most of them clustered in pulsotypes A and E. The data indicate the genetic complexity of S. suis 2 between herds and a close linkage among isolates from healthy sows and diseased pigs. Moreover, many factors, such as extensive use of tetracycline or diffusion of Tn916 with tetM, might have favored for the pathogenicity and widespread dissemination of S. suis serotype 2