A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images.

Congenital heart disease (CHD) is the most frequent birth defect and a leading cause of infant mortality, emphasizing the crucial need for its early diagnosis. Ultrasound is the primary imaging modality for prenatal CHD screening. As a complement to the four-chamber view, the three-vessel view (3VV) plays a vital role in detecting anomalies in the great vessels. However, the interpretation of fetal cardiac ultrasound images is subjective and relies heavily on operator experience, leading to variability in CHD detection rates, particularly in resource-constrained regions. In this study, we propose an automated method for segmenting the pulmonary artery, ascending aorta, and superior vena cava in the 3VV using a novel deep learning network named CoFi-Net. Our network incorporates a coarse-fine collaborative strategy with two parallel branches dedicated to simultaneous global localization and fine segmentation of the vessels. The coarse branch employs a partial decoder to leverage high-level semantic features, enabling global localization of objects and suppression of irrelevant structures. The fine branch utilizes attention-parameterized skip connections to improve feature representations and improve boundary information. The outputs of the two branches are fused to generate accurate vessel segmentations. Extensive experiments conducted on a collected dataset demonstrate the superiority of CoFi-Net compared to state-of-the-art segmentation models for 3VV segmentation, indicating its great potential for enhancing CHD diagnostic efficiency in clinical practice. Furthermore, CoFi-Net outperforms other deep learning models in breast lesion segmentation on a public breast ultrasound dataset, despite not being specifically designed for this task, demonstrating its potential and robustness for various segmentation tasks.

A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images.

BACKGROUND: Congenital heart disease (CHD) is one of the most common birth defects in the world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely intervention. Prenatal screening using ultrasound is the primary method for CHD detection. However, its effectiveness is heavily reliant on the expertise of physicians, leading to subjective interpretations and potential underdiagnosis. Therefore, a method for automatic analysis of fetal cardiac ultrasound images is highly desired to assist an objective and effective CHD diagnosis. METHOD: In this study, we propose a deep learning-based framework for the identification and segmentation of the three vessels-the pulmonary artery, aorta, and superior vena cava-in the ultrasound three vessel view (3VV) of the fetal heart. In the first stage of the framework, the object detection model Yolov5 is employed to identify the three vessels and localize the Region of Interest (ROI) within the original full-sized ultrasound images. Subsequently, a modified Deeplabv3 equipped with our novel AMFF (Attentional Multi-scale Feature Fusion) module is applied in the second stage to segment the three vessels within the cropped ROI images. RESULTS: We evaluated our method with a dataset consisting of 511 fetal heart 3VV images. Compared to existing models, our framework exhibits superior performance in the segmentation of all the three vessels, demonstrating the Dice coefficients of 85.55%, 89.12%, and 77.54% for PA, Ao and SVC respectively. CONCLUSIONS: Our experimental results show that our proposed framework can automatically and accurately detect and segment the three vessels in fetal heart 3VV images. This method has the potential to assist sonographers in enhancing the precision of vessel assessment during fetal heart examinations.

Implication of Amyloid Precursor-like Protein 2 Expression in Cutaneous Squamous Cell Carcinoma Pathogenesis.

BACKGROUND/AIM: Regulatory functions of amyloid precursor-like protein 2 (APLP2) expression in intracellular trafficking of major histocompatibility complex class I (MHC-I) and biological behavior of tumor cells have been reported in various types of malignancies but not in cutaneous squamous cell carcinoma (CSCC). This study aimed to investigate the role of APLP2 expression in the pathogenesis of CSCC. PATIENTS AND METHODS: The expression of APLP2 and a key modulator of cancer immune escape, MHC-I, were determined in CSCC tissue samples obtained from 141 patients using immunohistochemistry. The regulatory effects of APLP2 expression on the biological behavior and surface expression of MHC-I in CSCC cells were investigated by trypan blue assay, Matrigel invasion assay, and in vivo xenograft analysis. RESULTS: APLP2 immunoreactivity was high in 73 (51.8%) tissue samples from patients with CSCC and was significantly related to subcutaneous fat invasion and poor prognosis in our cohort. Moreover, proliferation of and invasion by CSCC cells were significantly reduced after APLP2 knockdown in CSCC cells both in vitro and in vivo. A significant association was found between APLP2 and membrane MHC-I expression in patients with CSCC. In vivo xenograft analysis showed that APLP2 knockdown increased membrane MHC-I expression in CSCC cells. CONCLUSION: APLP2 not only acts as an oncogene in CSCC progression but also as a possible modulator of cancer immune escape by influencing MHC-I expression on the cell surface. APLP2 may serve as a novel molecular biomarker and therapeutic target for patients with CSCC.

Proteasome Subunit Alpha Type-7 Expression Suppresses Cutaneous Squamous Cell Carcinoma Progression by Inhibiting Cancer-associated Cytokines.

BACKGROUND/AIM: Cutaneous squamous cell carcinoma (cSCC) is a common non-melanoma skin cancer, and its incidence is increasing. Proteasome subunit alpha type-7 (PSMA7) has been found to be aberrantly expressed in several cancers. However, whether it functions as a tumor suppressor or oncogene in the pathogenesis of cancers, particularly cSCC, remains controversial. Here, we aimed to investigate the functions of PSMA7 in cSCC pathogenesis. PATIENTS AND METHODS: Clinicopathological characteristics were evaluated in 131 patients with cSCC using tissue sections. The expression of PSMA7, nucleotide-binding oligomerization domain-containing protein 1 (NOD1), and mitochondrial antiviral signaling protein (MAVS) was determined in cSCC tissue sections using immunohistochemical staining. The effect of PSMA7 expression on the biological behavior of cSCC cells was investigated in vitro. RESULTS: High immunoreactivity of PSMA7 (high-PSMA7) was detected in 53 (40.5%) patients with cSCC and was significantly associated with histologic grade (p=0.008) and favorable recurrence-free survival (p=0.018). The expression of PSMA7 and NOD1 (p=0.026) and MAVS (p=0.032) was negatively correlated in cSCC tissues. Contrary to the results of the cohort study, cell viability and invasiveness significantly decreased after PSMA7 down-regulation in cSCC cells in vitro. mRNA expression of tumor necrosis factor-alpha, interleukin-1 alpha (IL-1α), IL-6, and IL-8 were significantly increased after PSMA7 down-regulation in cSCC cells (all p=0.002). CONCLUSION: PSMA7-mediated degradation of NOD1 and MAVS as well as the subsequent reduction of the cancer-associated cytokine network may be a crucial mechanism of the antitumoral function of PSMA7 in patients with cSCC.

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet.

Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor. Based on UNet, we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet). First, a Multi-scale Convolution Module is proposed. This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes, and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module. Second, a Cross-layer Attention Fusion Mechanism is proposed. This mechanism highlights tomato leaf disease locations via gating structure and fusion operation. Then, we employ SoftPool rather than MaxPool to retain valid information on tomato leaves. Finally, we use the SeLU function appropriately to avoid network neuron dropout. We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32% accuracy and 6.67M parameters. Our method achieves good results for tomato leaf disease segmentation, which demonstrates the effectiveness of the proposed methods.

Increasing a microscope's effective field of view via overlapped imaging and machine learning.

This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth product (SBP) of conventional microscopes. Here, we show both in simulation and experiment that overlapped imaging and co-designed analysis software can achieve accurate detection of diagnostically-relevant features for several applications, including counting of white blood cells and the malaria parasite, leading to multi-fold increase in detection and processing throughput with minimal reduction in accuracy.