Multi-view attribute learning and context relationship encoding enhanced segmentation of lung tumors from CT images.

Graph convolutional neural networks (GCN) have shown the promise in medical image segmentation due to the flexibility of representing diverse range of image regions using graph nodes and propagating knowledge via graph edges. However, existing methods did not fully exploit the various attributes of image nodes and the context relationship among their attributes. We propose a new segmentation method with multi-similarity view enhancement and node attribute context learning (MNSeg). First, multiple views were formed by measuring the similarities among the image nodes, and MNSeg has a GCN based multi-view image node attribute learning (MAL) module to integrate various node attributes learnt from multiple similarity views. Each similarity view contains the specific similarities among all the image nodes, and it was integrated with the node attributes from all the channels to form the enhanced attributes of image nodes. Second, the context relationships among the attributes of image nodes are formulated by a transformer-based context relationship encoding (CRE) strategy to propagate these relationships across all the image nodes. During the transformer-based learning, the relationships were estimated based on the self-attention on all the image nodes, and then they were encoded into the learned node features. Finally, we design an attention at attribute category level (ACA) to discriminate and fuse the learnt diverse information from MAL, CRE, and the original node attributes. ACA identifies the more informative attribute categories by adaptively learn their importance. We validate the performance of MNSeg on a public lung tumor CT dataset and an in-house non-small cell lung cancer (NSCLC) dataset collected from the hospital. The segmentation results show that MNSeg outperformed the compared segmentation methods in terms of spatial overlap and the shape similarities. The ablation studies demonstrated the effectiveness of MAL, CRE, and ACA. The generalization ability of MNSeg was proved by the consistent improved segmentation performances using different 3D segmentation backbones.

Topological structure and global features enhanced graph reasoning model for non-small cell lung cancer segmentation from CT.

Objective.Accurate and automated segmentation of lung tumors from computed tomography (CT) images is critical yet challenging. Lung tumors are of various sizes and locations and have indistinct boundaries adjacent to other normal tissues.Approach.We propose a new segmentation model that can integrate the topological structure and global features of image region nodes to address the challenges. Firstly, we construct a weighted graph with image region nodes. The graph topology reflects the complex spatial relationships among these nodes, and each node has its specific attributes. Secondly, we propose a node-wise topological feature learning module based on a new graph convolutional autoencoder (GCA). Meanwhile, a node information supplementation (GNIS) module is established by integrating specific features of each node extracted by a convolutional neural network (CNN) into each encoding layer of GCA. Afterwards, we construct a global feature extraction model based on multi-layer perceptron (MLP) to encode the features learnt from all the image region nodes which are crucial complementary information for tumor segmentation.Main results.Ablation study results over the public lung tumor segmentation dataset demonstrate the contributions of our major technical innovations. Compared with other segmentation methods, our new model improves the segmentation performance and has generalization ability on different 3D image segmentation backbones. Our model achieved Dice of 0.7827, IoU of 0.6981, and HD of 32.1743 mm on the public dataset 2018 Medical Segmentation Decathlon challenge, and Dice of 0.7004, IoU of 0.5704 and HD of 64.4661 mm on lung tumor dataset from Shandong Cancer Hospital.Significance. The novel model improves automated lung tumor segmentation performance especially the challenging and complex cases using topological structure and global features of image region nodes. It is of great potential to apply the model to other CT segmentation tasks.

Graph based multi-scale neighboring topology deep learning for kidney and tumor segmentation.

Objective.Effective learning and modelling of spatial and semantic relations between image regions in various ranges are critical yet challenging in image segmentation tasks.Approach.We propose a novel deep graph reasoning model to learn from multi-order neighborhood topologies for volumetric image segmentation. A graph is first constructed with nodes representing image regions and graph topology to derive spatial dependencies and semantic connections across image regions. We propose a new node attribute embedding mechanism to formulate topological attributes for each image region node by performing multi-order random walks (RW) on the graph and updating neighboring topologies at different neighborhood ranges. Afterwards, multi-scale graph convolutional autoencoders are developed to extract deep multi-scale topological representations of nodes and propagate learnt knowledge along graph edges during the convolutional and optimization process. We also propose a scale-level attention module to learn the adaptive weights of topological representations at multiple scales for enhanced fusion. Finally, the enhanced topological representation and knowledge from graph reasoning are integrated with content features before feeding into the segmentation decoder.Main results.The evaluation results over public kidney and tumor CT segmentation dataset show that our model outperforms other state-of-the-art segmentation methods. Ablation studies and experiments using different convolutional neural networks backbones show the contributions of major technical innovations and generalization ability.Significance.We propose for the first time an RW-driven MCG with scale-level attention to extract semantic connections and spatial dependencies between a diverse range of regions for accurate kidney and tumor segmentation in CT volumes.

Convolutional bi-directional learning and spatial enhanced attentions for lung tumor segmentation.

BACKGROUND AND OBJECTIVE: Accurate lung tumor segmentation from computed tomography (CT) is complex due to variations in tumor sizes, shapes, patterns and growing locations. Learning semantic and spatial relations between different feature channels, image regions and positions is critical yet challenging. METHODS: We propose a new segmentation method, PRCS, by learning and integrating multi-channel contextual relations, and spatial and position dependencies across image regions. Firstly, to extract contextual relationships between different deep image feature tensor channels, we propose a new convolutional bi-directional gated recurrent unit based module for forward and backward learning. Secondly, a novel cross-channel region-level attention mechanism is proposed to discriminate the contributions of different local regions and associated features in the global learning process. Finally, spatial and position dependencies are formulated by a new position-enhanced self-attention mechanism. The new attention can measure the diverse contributions of other positions to a target position and obtain an enhanced adaptive feature vector for the target position. RESULTS: Our model outperformed seven state-of-the-art segmentation methods on both public and in-house lung tumor datasets in terms of spatial overlapping and shape similarity. Ablation study results proved the effectiveness of three technical innovations and generalization ability on different 3D CNN segmentation backbones. CONCLUSION: The proposed model enhanced the learning and propagation of contextual, spatial and position relations in 3D volumes, improving lung tumours' segmentation performance with large variations and indistinct boundaries. PRCS provides an effective automated approach to support precision diagnosis and treatment planning of lung cancer.

Dendrocalamus latiflorus and its component rutin exhibit glucose-lowering activities by inhibiting hepatic glucose production via AKT activation.

The potential medicinal value of Ma bamboo (Dendrocalamus latiflorus), one of the most popular and economically important bamboo species in China, has been underestimated. In the present study, we found that D. latiflorus leaf extract (DLE) reduced fasting blood glucose levels, body weight, and low-density lipoprotein cholesterol with low liver toxicity in db/db mice. In addition, gene expression profiling was performed and pathway enrichment analysis showed that DLE affected metabolic pathways. Importantly, DLE activated the AKT signaling pathway and reduced glucose production by downregulating glucose-6-phosphatase (G6PC) and phosphoenolpyruvate carboxykinase 1 (PCK1) expression. Moreover, network pharmacology analysis identified rutin as an active component in DLE through targeting insulin growth factor 1 receptor (IGF1R), an upstream signaling transducer of AKT. Due to its hypoglycemic effects and low toxicity, DLE may be considered an adjuvant treatment option for type 2 diabetes patients.

Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales.

To evaluate the application of machine learning for the detection of subpleural pulmonary lesions (SPLs) in ultrasound (US) scans, we propose a novel boundary-restored network (BRN) for automated SPL segmentation to avoid issues associated with manual SPL segmentation (subjectivity, manual segmentation errors, and high time consumption). In total, 1612 ultrasound slices from 255 patients in which SPLs were visually present were exported. The segmentation performance of the neural network based on the Dice similarity coefficient (DSC), Matthews correlation coefficient (MCC), Jaccard similarity metric (Jaccard), Average Symmetric Surface Distance (ASSD), and Maximum symmetric surface distance (MSSD) was assessed. Our dual-stage boundary-restored network (BRN) outperformed existing segmentation methods (U-Net and a fully convolutional network (FCN)) for the segmentation accuracy parameters including DSC (83.45 ± 16.60%), MCC (0.8330 ± 0.1626), Jaccard (0.7391 ± 0.1770), ASSD (5.68 ± 2.70 mm), and MSSD (15.61 ± 6.07 mm). It also outperformed the original BRN in terms of the DSC by almost 5%. Our results suggest that deep learning algorithms aid fully automated SPL segmentation in patients with SPLs. Further improvement of this technology might improve the specificity of lung cancer screening efforts and could lead to new applications of lung US imaging.

Manipulation of polyhydroxyalkanoate granular sizes in Halomonas bluephagenesis.

Bacterial polyhydroxyalkanoates (PHA) are a family of intracellular polyester granules with sizes ranging from 100 to 500 nm. Due to their small sizes, it has been very difficult to separate the PHA granules from the bacterial broths. This study aims to engineer the PHA size control mechanism to obtain large PHA granular sizes beneficial for the separation. It has been reported that phasin (PhaP) is an amphiphilic protein located on the surface of PHA granules functioning to regulate sizes and numbers of PHA granules in bacterial cells, deletions on PhaPs result in reduced PHA granule number and enhanced granule sizes. Three genes phaP1, phaP2 and phaP3 encoding three PhaP proteins were deleted in various combinations in halophilic bacterium Halomonas bluephagenesis TD01. The phaP1-knockout strain generated much larger PHA granules with almost the same size as their producing cells without significantly affecting the PHA accumulation yet with a reduced PHA molecular weights. In contrast, the phaP2- and phaP3-knockout strains produced slightly larger sizes of PHA granules with increased PHA molecular weights. While PHA accumulation by phaP3-knockout strains showed a significant reduction. All of the PhaP deletion efforts could not form PHA granules larger than a normal size of H. bluephagenesis TD01. It appears that the PHA granular sizes could be limited by bacterial cell sizes. Therefore, genes minC and minD encoding proteins that block formation of cell fission rings (Z-rings) were over-expressed in various phaP deleted H. bluephagenesis TD01, resulting in large cell sizes of H. bluephagenesis TD01 containing PHA granules with sizes of up to 10 µm that has never been observed previously. It can be concluded that PHA granule sizes are limited by the cell sizes. By engineering a large cell morphology large PHA granules can be produced by PhaP deleted mutants.

Promoter Engineering for Enhanced P(3HB- co-4HB) Production by Halomonas bluephagenesis.

Promoters for the expression of heterologous genes in Halomonas bluephagenesis are quite limited, and many heterologous promoters function abnormally in this strain. Pporin, a promoter of the strongest expressed protein porin in H. bluephagenesis, is one of the few promoters available for heterologous expression in H. bluephagenesis, yet it has a fixed transcriptional activity that cannot be tuned. A stable promoter library with a wide range of activities is urgently needed. This study reports an approach to construct a promoter library based on the Pporin core region, namely, from the -35 box to the transcription start site, a spacer and an insulator. Saturation mutagenesis was conducted inside the promoter core region to significantly increase the diversity within the promoter library. The promoter library worked in both E. coli and H. bluephagenesis, covering a wide range of relative transcriptional strengths from 40 to 140 000. The library is therefore suitable for the transcription of many different heterologous genes, serving as a platform for protein expression and fine-tuned metabolic engineering of H. bluephagenesis TD01 and its derivative strains. H. bluephagenesis strains harboring the orfZ gene encoding 4HB-CoA transferase driven by selected promoters from the library were constructed, the best one produced over 100 g/L cell dry weight containing 80% poly(3-hydroxybutyrate- co-11 mol % 4-hydroxybutyrate) with a productivity of 1.59 g/L/h after 50 h growth under nonsterile fed-batch conditions. This strain was found the best for P(3HB- co-4HB) production in the laboratory scale.