Transcriptome analysis reveals that PbMYB61 and PbMYB308 are involved in the regulation of lignin biosynthesis in pear fruit stone cells.

Pear fruit stone cells have thick walls and are formed by the secondary deposition of lignin in the primary cell wall of thin-walled cells. Their content and size seriously affect fruit characteristics related to edibility. To reveal the regulatory mechanism underlying stone cell formation during pear fruit development and to identify hub genes, we examined the stone cell and lignin contents of 30 'Shannongsu' pear flesh samples and analyzed the transcriptomes of 15 pear flesh samples collected at five developmental stages. On the basis of the RNA-seq data, 35 874 differentially expressed genes were detected. Additionally, two stone cell-related modules were identified according to a WGCNA. A total of 42 lignin-related structural genes were subsequently obtained. Furthermore, nine hub structural genes were identified in the lignin regulatory network. We also identified PbMYB61 and PbMYB308 as candidate transcriptional regulators of stone cell formation after analyzing co-expression networks and phylogenetic relationships. Finally, we experimentally validated and characterized the candidate transcription factors and revealed that PbMYB61 regulates stone cell lignin formation by binding to the AC element in the PbLAC1 promoter to upregulate expression. However, PbMYB308 negatively regulates stone cell lignin synthesis by binding to PbMYB61 to form a dimer that cannot activate PbLAC1 expression. In this study, we explored the lignin synthesis-related functions of MYB family members. The results presented herein are useful for elucidating the complex mechanisms underlying lignin biosynthesis during pear fruit stone cell development.

Analysis of bZIP gene family in lotus (Nelumbo) and functional study of NnbZIP36 in regulating anthocyanin synthesis.

BACKGROUND: The basic leucine zipper (bZIP) family is a predominant group of transcription factors in plants, involved in regulating plant growth, development, and response to stressors. Additionally, the bZIP gene family has a key role in anthocyanin production. Despite the significant role of bZIP genes in plants, their potential contribution in lotus remains understudied. RESULTS: A total of 124 bZIP genes (59 NnbZIPs and 65 NlbZIPs) were identified from genomes of two lotus species. These genes were classified into 13 groups according to the grouping principle of the Arabidopsis bZIP gene family. Analysis of promoter cis-acting elements indicated that most bZIP gene family members in lotus are associated with response to abiotic stresses. The promoters of some bZIP genes contain MYB binding sites that regulate anthocyanin synthesis. We examined the anthocyanin content of the petals from three different colored lotus, combined with transcriptome data analysis and qRT-PCR results, showing that the expression trends of NnbZIP36 and the homologous gene NlbZIP38 were significantly correlated with the anthocyanin content in lotus petals. Furthermore, we found that overexpression of NnbZIP36 in Arabidopsis promoted anthocyanin accumulation by upregulating the expression of genes (4CL, CHI, CHS, F3H, F3'H, DFR, ANS and UF3GT) related to anthocyanin synthesis. CONCLUSIONS: Our study enhances the understanding of the bZIP gene family in lotus and provides evidence for the role of NnbZIP36 in regulating anthocyanin synthesis. This study also sets the stage for future investigations into the mechanism by which the bZIP gene family regulates anthocyanin biosynthesis in lotus.

The regulatory module MdBZR1-MdCOL6 mediates brassinosteroid- and light-regulated anthocyanin synthesis in apple.

The anthocyanin content is an important indicator of the nutritional value of most fruits, including apple (Malus domestica). Anthocyanin synthesis is coordinately regulated by light and various phytohormones. In this study on apple, we revealed the antagonistic relationship between light and brassinosteroid (BR) signaling pathways, which is mediated by BRASSINAZOLE-RESISTANT 1 (MdBZR1) and the B-box protein MdCOL6. The exogenous application of brassinolide inhibited the high-light-induced anthocyanin accumulation in red-fleshed apple seedlings, whereas increases in the light intensity decreased the endogenous BR content. The overexpression of MdBZR1 inhibited the anthocyanin synthesis in apple plants. An exposure to a high-light intensity induced the degradation of dephosphorylated MdBZR1, resulting in functional impairment. MdBZR1 was identified as an upstream repressor of MdCOL6, which promotes anthocyanin synthesis in apple plants. Furthermore, MdBZR1 interacts with MdCOL6 to attenuate its ability to activate MdUFGT and MdANS transcription. Thus, MdBZR1 negatively regulates MdCOL6-mediated anthocyanin accumulation. Our study findings have clarified the molecular basis of the integration of light and BR signals during the regulation of anthocyanin biosynthesis, which is an important process influencing fruit quality.

Ethylene increases the cold tolerance of apple via the MdERF1B-MdCIbHLH1 regulatory module.

Cold stress has always been a major abiotic factor affecting the yield and quality of temperate fruit crops. Ethylene plays a critical regulatory role in the cold stress response, but the underlying molecular mechanisms remain elusive. Here, we revealed that ethylene positively modulates apple responses to cold stress. Treatment with 1-aminocyclopropane-1-carboxylate (an ethylene precursor) and aminoethoxyvinylglycine (an ethylene biosynthesis inhibitor) respectively increased and decreased the cold tolerance of apple seedlings. Consistent with the positive effects of ethylene on cold stress responses, a low-temperature treatment rapidly induced ethylene release and the expression of MdERF1B, which encodes an ethylene signaling activator, in apple seedlings. Overexpression of MdERF1B significantly increased the cold tolerance of apple plant materials (seedlings and calli) and Arabidopsis thaliana seedlings. A quantitative real-time PCR analysis indicated that MdERF1B upregulates the expression of the cold-responsive gene MdCBF1 in apple seedlings. Moreover, MdCIbHLH1, which functions upstream of CBF-dependent pathways, enhanced the binding of MdERF1B to target gene promoters as well as the consequent transcriptional activation. The stability of MdERF1B-MdCIbHLH1 was affected by cold stress and ethylene. Furthermore, MdERF1B interacted with the promoters of two genes critical for ethylene biosynthesis, MdACO1 and MdERF3. The resulting upregulated expression of these genes promoted ethylene production. However, the downregulated MdCIbHLH1 expression in MdERF1B-overexpressing apple calli significantly inhibited ethylene production. These findings imply that MdERF1B-MdCIbHLH1 is a potential regulatory module that integrates the cold and ethylene signaling pathways in apple.

Brassinolide inhibits flavonoid biosynthesis and red-flesh coloration via the MdBEH2.2-MdMYB60 complex in apple.

Flavonoid content, which is an important indicator of the nutritional value of fruits and vegetables, directly determines the marketability of many fruit crops, including apple (Malus domestica). Brassinosteroids (BRs) are steroid hormones that affect flavonoid biosynthesis in plants, but the underlying regulatory mechanism remains unclear. In this study, treatments with brassinolide (the most active BR) and brassinazole (a BR biosynthesis inhibitor) decreased and increased, respectively, the flavonoid, anthocyanin, and proanthocyanidin (PA) content in red-fleshed apple seedlings and calli. We subsequently demonstrated that a BZR (BRI1-EMS-suppressor (BES)/brassinazole-resistant) family transcription factor, MdBEH2.2, participates in BR-regulated flavonoid biosynthesis. Specifically, MdBEH2.2 inhibits the accumulation of flavonoids, anthocyanins, and PAs in apple seedlings; however, brassinazole treatment weakens the inhibitory effect. Additionally, we confirmed that a BR-induced MYB TF, MdMYB60, interacts with MdBEH2.2. The resulting MdBEH2.2-MdMYB60 complex further enhances the inhibitory effect of MdBEH2.2 or MdMYB60 on the transcription of flavonoid biosynthesis-related genes. These results indicate that brassinolide decreases flavonoid content through the MdBEH2.2-MdMYB60 regulatory module. Our findings further clarify the molecular mechanism mediating the regulation of flavonoid biosynthesis by BR signals in horticultural crops.

A feedback loop involving MdMYB108L and MdHY5 controls apple cold tolerance.

The MYB transcription factors are important for many aspects of plant stress responses. In this study, we isolated and identified an apple MYB gene, MdMYB108L, whose expression is induced by light and cold stresses. An analysis of MdMYB108L-overexpressing transgenic apple calli revealed that MdMYB108L enhances cold tolerance in apple by upregulating MdCBF3 expression. Interestingly, the expression of MdHY5, which encodes an integrator of light and cold signals, was significantly downregulated in transgenic calli. Yeast one-hybrid and electrophoretic mobility shift assays indicated that MdMYB108L positively regulates cold tolerance by binding to the MdCBF3 promoter. Additionally, MdHY5 functions upstream of MdMYB108L, and the resulting increase in MdMYB108L abundance downregulates MdHY5 transcription. The results of this study elucidate a new pathway for the regulation of apple cold tolerance via a feedback mechanism involving MdMYB108L and MdHY5.

The status and developments of leather solid waste treatment: A mini-review.

Leather making is one of the most widespread industries in the world. The production of leather goods generates different types of solid wastes and wastewater. These wastes will pollute the environment and threat the health of human beings if they are not well treated. Consequently, the treatment of pollution caused by the wastes from leather tanning is really important. In comparison with the disposal of leather wastewater, the treatment of leather solid wastes is more intractable. Hence, the treatment of leather solid wastes needs more innovations. To keep up with the rapid development of the modern leather industry, various innovative techniques have been newly developed. In this mini-review article, the major achievements in the treatment of leather solid wastes are highlighted. Emphasis will be placed on the treatment of chromium-tanned solid wastes; some new approaches are also discussed. We hope that this mini-review can provide some valuable information to promote the broad understanding and effective treatment of leather solid wastes in the leather industry.

A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features.

BACKGROUND: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effective treatment plans for patients. Notably, lymphoma comprises subtypes like diffuse large B-cell lymphoma and Hodgkin's lymphoma, while lung cancer encompasses adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. Similarly, liver cancer consists of subtypes such as cholangiocarcinoma and hepatocellular carcinoma. Consequently, the subtype classification of tumors based on PET images holds immense clinical significance. However, in clinical practice, the number of cases available for each subtype is often limited and imbalanced. Therefore, the primary challenge lies in achieving precise subtype classification using a small dataset. METHOD: This paper presents a novel approach for tumor subtype classification in small datasets using RA-DL (Radiomics-DeepLearning) attention. To address the limited sample size, Support Vector Machines (SVM) is employed as the classifier for tumor subtypes instead of deep learning methods. Emphasizing the importance of texture information in tumor subtype recognition, radiomics features are extracted from the tumor regions during the feature extraction stage. These features are compressed using an autoencoder to reduce redundancy. In addition to radiomics features, deep features are also extracted from the tumors to leverage the feature extraction capabilities of deep learning. In contrast to existing methods, our proposed approach utilizes the RA-DL-Attention mechanism to guide the deep network in extracting complementary deep features that enhance the expressive capacity of the final features while minimizing redundancy. To address the challenges of limited and imbalanced data, our method avoids using classification labels during deep feature extraction and instead incorporates 2D Region of Interest (ROI) segmentation and image reconstruction as auxiliary tasks. Subsequently, all lesion features of a single patient are aggregated into a feature vector using a multi-instance aggregation layer. RESULT: Validation experiments were conducted on three PET datasets, specifically the liver cancer dataset, lung cancer dataset, and lymphoma dataset. In the context of lung cancer, our proposed method achieved impressive performance with Area Under Curve (AUC) values of 0.82, 0.84, and 0.83 for the three-classification task. For the binary classification task of lymphoma, our method demonstrated notable results with AUC values of 0.95 and 0.75. Moreover, in the binary classification task of liver tumor, our method exhibited promising performance with AUC values of 0.84 and 0.86. CONCLUSION: The experimental results clearly indicate that our proposed method outperforms alternative approaches significantly. Through the extraction of complementary radiomics features and deep features, our method achieves a substantial improvement in tumor subtype classification performance using small PET datasets.

CoF-DResNet: Cancer Metastasis Recognition Network based on Dynamic Coordinated Metabolic Attention and Structural Attention.

BACKGROUND: Cancer metastasis usually means that cancer cells spread to other tissues or organs, and the condition worsens. Identifying whether cancer has metastasized can help doctors infer the progression of a patient's condition and is an essential prerequisite for devising treatment plans. Fluorine 18 fluorodeoxyglucose positron emission tomography/computed tomography ( 18F -FDG PET/CT) is an advanced cancer diagnostic imaging technique that provides both metabolic and structural information. METHOD: In cancer metastasis recognition tasks, effectively integrating metabolic and structural information stands as a key technology to enhance feature representation and recognition performance. This paper proposes a cancer metastasis identification network based on dynamic coordinated metabolic attention and structural attention to address these challenges. Specifically, metabolic and structural features are extracted by incorporating a dynamic coordinated attention module (DCAM) into two branches of ResNet networks, thereby amalgamating high metabolic spatial information from PET images with texture structure information from CT images, and dynamically adjusting this process through iterations. DISCUSSION: Next, to improve the efficacy of feature expression, a multi-receptive field feature fusion module (MRFM) is included in order to execute multi-receptive field fusion of semantic features. RESULT: To validate the effectiveness of our proposed model, experiments were conducted on both a private lung lymph nodes dataset and a public soft tissue sarcomas dataset. CONCLUSION: The accuracy of our method reached 76.0% and 75.1% for the two datasets, respectively, demonstrating an improvement of 6.8% and 5.6% compared to ResNet, thus affirming the efficacy of our method.

ECPC-IDS: A benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions.

BACKGROUND: Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis and reducing the workload of doctors. However, the absence of publicly available image datasets restricts the application of computer-assisted diagnostic techniques. METHODS: In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with 7159 images in multiple formats totally. In order to prove the effectiveness of segmentation on ECPC-IDS, six deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with 3579 images and XML files with annotation information totally. Eight deep learning methods are selected for experiments on the detection task. RESULTS: This study is conduct using deep learning-based semantic segmentation and object detection methods to demonstrate the distinguishability on ECPC-IDS. From a separate perspective, the minimum and maximum values of Dice on PET images are 0.546 and 0.743, respectively. The minimum and maximum values of Dice on CT images are 0.012 and 0.510, respectively. The target detection section's maximum mAP values on PET and CT images are 0.993 and 0.986, respectively. CONCLUSION: As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multi-modality images. ECPC-IDS can assist researchers in exploring new algorithms to enhance computer-assisted diagnosis, benefiting both clinical doctors and patients. ECPC-IDS is also freely published for non-commercial at: https://figshare.com/articles/dataset/ECPC-IDS/23808258.

PST-Radiomics: a PET/CT lymphoma classification method based on pseudo spatial-temporal radiomic features and structured atrous recurrent convolutional neural network.

Objective.Existing radiomic methods tend to treat each isolated tumor as an inseparable whole, when extracting radiomic features. However, they may discard the critical intra-tumor metabolic heterogeneity (ITMH) information, that contributes to triggering tumor subtypes. To improve lymphoma classification performance, we propose a pseudo spatial-temporal radiomic method (PST-Radiomics) based on positron emission tomography computed tomography (PET/CT).Approach.Specifically, to enable exploitation of ITMH, we first present a multi-threshold gross tumor volume sequence (GTVS). Next, we extract 1D radiomic features based on PET images and each volume in GTVS and create a pseudo spatial-temporal feature sequence (PSTFS) tightly interwoven with ITMH. Then, we reshape PSTFS to create 2D pseudo spatial-temporal feature maps (PSTFM), of which the columns are elements of PSTFS. Finally, to learn from PSTFM in an end-to-end manner, we build a light-weighted pseudo spatial-temporal radiomic network (PSTR-Net), in which a structured atrous recurrent convolutional neural network serves as a PET branch to better exploit the strong local dependencies in PSTFM, and a residual convolutional neural network is used as a CT branch to exploit conventional radiomic features extracted from CT volumes.Main results.We validate PST-Radiomics based on a PET/CT lymphoma subtype classification task. Experimental results quantitatively demonstrate the superiority of PST-Radiomics, when compared to existing radiomic methods.Significance.Feature map visualization of our method shows that it performs complex feature selection while extracting hierarchical feature maps, which qualitatively demonstrates its superiority.

ASE-Net: A tumor segmentation method based on image pseudo enhancement and adaptive-scale attention supervision module.

Fluorine 18(18F) fluorodeoxyglucose positron emission tomography and Computed Tomography (PET/CT) is the preferred imaging method of choice for the diagnosis and treatment of many cancers. However, factors such as low-contrast organ and tissue images, and the original scale of tumors pose huge obstacles to the accurate segmentation of tumors. In this work, we propose a novel model ASE-Net which is used for multimodality tumor segmentation. Firstly, we propose a pseudo-enhanced CT image generation method based on metabolic intensity to generate pseudo-enhanced CT images as additional input, which reduces the learning of the network in the spatial position of PET/CT and increases the discriminability of the corresponding structural positions of the high and low metabolic region. Second, unlike previous networks that directly segment tumors of all scales, we propose an Adaptive-Scale Attention Supervision Module at the skip connections, after combining the results of all paths, tumors of different scales will be given different receptive fields. Finally, Dual Path Block is used as the backbone of our network to leverage the ability of residual learning for feature reuse and dense connection for exploring new features. Our experimental results on two clinical PET/CT datasets demonstrate the effectiveness of our proposed network and achieve 78.56% and 72.57% in Dice Similarity Coefficient, respectively, which has better performance compared to state-of-the-art network models, whether for large or small tumors. The proposed model will help pathologists formulate more accurate diagnoses by providing reference opinions during diagnosis, consequently improving patient survival rate.

CGBO-Net: Cruciform structure guided and boundary-optimized lymphoma segmentation network.

Lymphoma segmentation plays an important role in the diagnosis and treatment of lymphocytic tumor. Most current existing automatic segmentation methods are difficult to give precise tumor boundary and location. Semi-automatic methods are usually combined with manually added features such as bounding box or points to locate the tumor. Inspired by this, we propose a cruciform structure guided and boundary-optimized lymphoma segmentation network(CGBS-Net). The method uses a cruciform structure extracted based on PET images as an additional input to the network, while using a boundary gradient loss function to optimize the boundary of the tumor. Our method is divided into two main stages: In the first stage, we use the proposed axial context-based cruciform structure extraction (CCE) method to extract the cruciform structures of all tumor slices. In the second stage, we use PET/CT and the corresponding cruciform structure as input in the designed network (CGBO-Net) to extract tumor structure and boundary information. The Dice, Precision, Recall, IOU and RVD are 90.7%, 89.4%, 92.5%, 83.1% and 4.5%, respectively. Validate on the lymphoma dataset and publicly available head and neck data, our proposed approach is better than the other state-of-the-art semi-segmentation methods, which produces promising segmentation results.

C2BA-UNet: A context-coordination multi-atlas boundary-aware UNet-like method for PET/CT images based tumor segmentation.

Tumor segmentation is a necessary step in clinical processing that can help doctors diagnose tumors and plan surgical treatments. Since tumors are usually small, the locations and appearances vary substantially across individuals, and the contrast between tumors and adjacent normal tissues is low, tumor segmentation is still a challenging task. Although convolutional neural networks (CNNs) have achieved good results in tumor segmentation, the information about tumor boundaries has been rarely explored. To solve the problem, this paper proposes a new method for automatic tumor segmentation in PET/CT images based on context-coordination and boundary-aware, termed as C2BA-UNet. We employ a UNet-like backbone network and replace the encoder with EfficientNet-B0 for efficiency. To acquire potential tumor boundaries, we propose a new multi-atlas boundary-aware (MABA) module based on gradient atlas, uncertainty atlas, and level set atlas, that focuses on uncertain regions between tumors and adjacent tissues. Furthermore, we propose a new context coordination module (CCM) to combine multi-scale context information with attention mechanism to optimize skip connection in high-level layers. To validate the superiority of our method, we conduct experiments on a publicly available soft tissue sarcoma (STS) dataset and a lymphoma dataset, and the results show our method is competitive with other comparison methods.

Leverage prior texture information in deep learning-based liver tumor segmentation: A plug-and-play Texture-Based Auto Pseudo Label module.

Segmenting the liver and tumor regions using CT scans is crucial for the subsequent treatment in clinical practice and radiotherapy. Recently, liver and tumor segmentation techniques based on U-Net have gained popularity. However, there are numerous varieties of liver tumors, and they differ greatly in terms of their shapes and textures. It is unreasonable to regard all liver tumors as one class for learning. Meanwhile, texture information is crucial for the identification of liver tumors. We propose a plug-and-play Texture-based Auto Pseudo Label (TAPL) module to take use of the texture information of tumors and enable the neural network actively learn the texture differences between various tumors to increase the segmentation accuracy, especially for small tumors. The TPAL module consists of two parts, texture enhancement and texture-based pseudo label generator. To highlight the regions where the texture varies significantly, we enhance the textured areas of the CT image. Based on their texture information, tumors are automatically divided into several classes by the texture-based pseudo label generator. The multi-class tumors produced by the neural network during the prediction step are combined into a single tumor label, which is then used as the outcome of the segmentation. Experiments on clinical dataset and public dataset Lits2017 show that the proposed algorithm outperforms single liver tumor label segmentation methods and is more friendly to small tumors.

MSRA-Net: Tumor segmentation network based on Multi-scale Residual Attention.

Automatic Medical segmentation of medical images is an important part in the field of computer medical diagnosis, among which tumor segmentation is an important branch of medical image segmentation. Accurate automatic segmentation method is very important in medical diagnosis and treatment. Positron emission computed tomography (PET) and X-ray computed tomography (CT) images are widely used in medical image segmentation to help doctors accurately locate information such as tumor location and shape, providing metabolic and anatomical information, respectively. At present, PET/CT images have not been effectively combined in the research of medical image segmentation, and the complementary semantic information between the superficial and deep layers of neural network has not been ensured. To solve the above problems, this paper proposed a Multi-scale Residual Attention network (MSRA-Net) for tumor segmentation of PET/CT. We first use an attention-fusion based approach to automatically learn the tumor-related areas of PET images and weaken the irrelevant area. Then, the segmentation results of PET branch are processed to optimize the segmentation results of CT branch by using attention mechanism. The proposed neural network (MSRA-Net) can effectively fuse PET image and CT image, which can improve the precision of tumor segmentation by using complementary information of the multi-modal image, and reduce the uncertainty of single modal image segmentation. Proposed model uses multi-scale attention mechanism and residual module, which fuse multi-scale features to form complementary features of different scales. We compare with state-of-the-art medical image segmentation methods. The experiment showed that the Dice coefficient of the proposed network in soft tissue sarcoma and lymphoma datasets increased by 8.5% and 6.1% respectively compared with UNet, showing a significant improvement.

SDA-UNet: a hepatic vein segmentation network based on the spatial distribution and density awareness of blood vessels.

Objective.Hepatic vein segmentation is a fundamental task for liver diagnosis and surgical navigation planning. Unlike other organs, the liver is the only organ with two sets of venous systems. Meanwhile, the segmentation target distribution in the hepatic vein scene is extremely unbalanced. The hepatic veins occupy a small area in abdominal CT slices. The morphology of each person's hepatic vein is different, which also makes segmentation difficult. The purpose of this study is to develop an automated hepatic vein segmentation model that guides clinical diagnosis.Approach.We introduce the 3D spatial distribution and density awareness (SDA) of hepatic veins and propose an automatic segmentation network based on 3D U-Net which includes a multi-axial squeeze and excitation module (MASE) and a distribution correction module (DCM). The MASE restrict the activation area to the area with hepatic veins. The DCM improves the awareness of the sparse spatial distribution of the hepatic veins. To obtain global axial information and spatial information at the same time, we study the effect of different training strategies on hepatic vein segmentation. Our method was evaluated by a public dataset and a private dataset. The Dice coefficient achieves 71.37% and 69.58%, improving 3.60% and 3.30% compared to the other SOTA models, respectively. Furthermore, metrics based on distance and volume also show the superiority of our method.Significance.The proposed method greatly reduced false positive areas and improved the segmentation performance of the hepatic vein in CT images. It will assist doctors in making accurate diagnoses and surgical navigation planning.

A Lightweight Network for Contextual and Morphological Awareness for Hepatic Vein Segmentation.

Accurate segmentation of the hepatic vein can improve the precision of liver disease diagnosis and treatment. Since the hepatic venous system is a small target and sparsely distributed, with various and diverse morphology, data labeling is difficult. Therefore, automatic hepatic vein segmentation is extremely challenging. We propose a lightweight contextual and morphological awareness network and design a novel morphology aware module based on attention mechanism and a 3D reconstruction module. The morphology aware module can obtain the slice similarity awareness mapping, which can enhance the continuous area of the hepatic veins in two adjacent slices through attention weighting. The 3D reconstruction module connects the 2D encoder and the 3D decoder to obtain the learning ability of 3D context with a very small amount of parameters. Compared with other SOTA methods, using the proposed method demonstrates an enhancement in the dice coefficient with few parameters on the two datasets. A small number of parameters can reduce hardware requirements and potentially have stronger generalization, which is an advantage in clinical deployment.

MRLA-Net: A tumor segmentation network embedded with a multiple receptive-field lesion attention module in PET-CT images.

The tumor image segmentation is an important basis for doctors to diagnose and formulate treatment planning. PET-CT is an extremely important technology for recognizing the systemic situation of diseases due to the complementary advantages of their respective modal information. However, current PET-CT tumor segmentation methods generally focus on the fusion of PET and CT features. The fusion of features will weaken the characteristics of the modality itself. Therefore, enhancing the modal features of the lesions can obtain optimized feature sets, which is extremely necessary to improve the segmentation results. This paper proposed an attention module that integrates the PET-CT diagnostic visual field and the modality characteristics of the lesion, that is, the multiple receptive-field lesion attention module. This paper made full use of the spatial domain, frequency domain, and channel attention, and proposed a large receptive-field lesion localization module and a small receptive-field lesion enhancement module, which together constitute the multiple receptive-field lesion attention module. In addition, a network embedded with a multiple receptive-field lesion attention module has been proposed for tumor segmentation. This paper conducted experiments on a private liver tumor dataset as well as two publicly available datasets, the soft tissue sarcoma dataset, and the head and neck tumor segmentation dataset. The experimental results showed that the proposed method achieves excellent performance on multiple datasets, and has a significant improvement compared with DenseUNet, and the tumor segmentation results on the above three PET/CT datasets were improved by 7.25%, 6.5%, 5.29% in Dice per case. Compared with the latest PET-CT liver tumor segmentation research, the proposed method improves by 8.32%.

MSU-Net: Multi-scale Sensitive U-Net based on pixel-edge-region level collaborative loss for nasopharyngeal MRI segmentation.

Radiotherapy is the traditional treatment of early nasopharyngeal carcinoma (NPC). Automatic accurate segmentation of risky lesions in the nasopharynx is crucial in radiotherapy. U-Net has been proved its effective medical image segmentation ability. However, the great difference in the structure and size of nasopharynx among different patients requires a network that pays more attention to multi-scale information. In this paper, we propose a multi-scale sensitive U-Net (MSU-Net) based on pixel-edge-region level collaborative loss (LCo-PER) for NPC segmentation task. A series of novel feature fusion modules based on spatial continuity and multi-scale semantic are proposed for extracting multi-level features while efficiently searching for all size lesions. A spatial continuity information extraction module (SCIEM) is proposed for effectively using the spatial continuity information of context slices to search small lesions. And a multi-scale semantic feature extraction module (MSFEM) is proposed for extracting features of different receptive fields. LCo-PER is proposed for the network training which makes network model could take into account the size of different lesions. The global Dice, Precision, Recall and IOU of the testing set are 84.50%, 97.48%, 84.33% and 82.41%, respectively. The results show that our method is better than the other state-of-the-art methods for NPC segmentation which obtain higher accuracy and effective segmentation performance.