Three-Dimensional Medical Image Fusion with Deformable Cross-Attention

Multimodal medical image fusion plays an instrumental role in several areas of medical image processing, particularly in disease recognition and tumor detection. Traditional fusion methods tend to process each modality independently before combining the features and reconstructing the fusion image. However, this approach often neglects the fundamental commonalities and disparities between multimodal information. Furthermore, the prevailing methodologies are largely confined to fusing two-dimensional (2D) medical image slices, leading to a lack of contextual supervision in the fusion images and subsequently, a decreased information yield for physicians relative to three-dimensional (3D) images. In this study, we introduce an innovative unsupervised feature mutual learning fusion network designed to rectify these limitations. Our approach incorporates a Deformable Cross Feature Blend (DCFB) module that facilitates the dual modalities in discerning their respective similarities and differences. We have applied our model to the fusion of 3D MRI and PET images obtained from 660 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Through the application of the DCFB module, our network generates high-quality MRI-PET fusion images. Experimental results demonstrate that our method surpasses traditional 2D image fusion methods in performance metrics such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Importantly, the capacity of our method to fuse 3D images enhances the information available to physicians and researchers, thus marking a significant step forward in the field. The code will soon be available online

A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images

Pathologists need to combine information from differently stained pathology slices for accurate diagnosis. Deformable image registration is a necessary technique for fusing multi-modal pathology slices. This paper proposes a hybrid deep feature-based deformable image registration framework for stained pathology samples. We first extract dense feature points via the detector-based and detector-free deep learning feature networks and perform points matching. Then, to further reduce false matches, an outlier detection method combining the isolation forest statistical model and the local affine correction model is proposed. Finally, the interpolation method generates the deformable vector field for pathology image registration based on the above matching points. We evaluate our method on the dataset of the Non-rigid Histology Image Registration (ANHIR) challenge, which is co-organized with the IEEE ISBI 2019 conference. Our technique outperforms the traditional approaches by 17% with the Average-Average registration target error (rTRE) reaching 0.0034. The proposed method achieved state-of-the-art performance and ranked 1st in evaluating the test dataset. The proposed hybrid deep feature-based registration method can potentially become a reliable method for pathology image registration.Comment: 22 pages, 12 figures. 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

Three new species of Trichoderma (Hypocreales, Hypocreaceae) from soils in China

Trichoderma spp. are diverse fungi with wide distribution. In this study, we report on three new species of Trichoderma, namely T. nigricans, T. densissimum and T. paradensissimum, collected from soils in China. Their phylogenetic position of these novel species was determined by analyzing the concatenated sequences of the second largest nuclear RNA polymerase subunit encoding gene (rpb2) and the translation elongation factor 1– alpha encoding gene (tef1). The results of the phylogenetic analysis showed that each new species formed a distinct clade: T. nigricans is a new member of the Atroviride Clade, and T. densissimum and T. paradensissimum belong to the Harzianum Clade. A detailed description of the morphology and cultural characteristics of the newly discovered Trichoderma species is provided, and these characteristics were compared with those of closely related species to better understand the taxonomic relationships within the Trichoderma

Differential Communications between Fungi and Host Plants Revealed by Secretome Analysis of Phylogenetically Related Endophytic and Pathogenic Fungi

<div><p>During infection, both phytopathogenic and endophytic fungi form intimate contact with living plant cells, and need to resist or disable host defences and modify host metabolism to adapt to their host. Fungi can achieve these changes by secreting proteins and enzymes. A comprehensive comparison of the secretomes of both endophytic and pathogenic fungi can improve our understanding of the interactions between plants and fungi. Although <i>Magnaporthe oryzae</i>, <i>Gaeumannomyces graminis</i>, and <i>M</i>. <i>poae</i> are economically important fungal pathogens, and the related species <i>Harpophora oryzae</i> is an endophyte, they evolved from a common pathogenic ancestor. We used a pipeline analysis to predict the <i>H</i>. <i>oryzae</i>, <i>M</i>. <i>oryzae</i>, <i>G</i>. <i>graminis</i>, and <i>M</i>. <i>poae</i> secretomes and identified 1142, 1370, 1001, and 974 proteins, respectively. Orthologue gene analyses demonstrated that the <i>M</i>. <i>oryzae</i> secretome evolved more rapidly than those of the other three related species, resulting in many species-specific secreted protein-encoding genes, such as avirulence genes. Functional analyses highlighted the abundance of proteins involved in the breakdown of host plant cell walls and oxidation-reduction processes. We identified three novel motifs in the <i>H</i>. and <i>M</i>. <i>oryzae</i> secretomes, which may play key roles in the interaction between rice and <i>H</i>. <i>oryzae</i>. Furthermore, we found that expression of the <i>H</i>. <i>oryzae</i> secretome involved in plant cell wall degradation was downregulated, but the <i>M</i>. <i>oryzae</i> secretome was upregulated with many more upregulated genes involved in oxidation-reduction processes. The divergent <i>in planta</i> expression patterns of the <i>H</i>. and <i>M</i>. <i>oryzae</i> secretomes reveal differences that are associated with mutualistic and pathogenic interactions, respectively.</p></div

Secreted proteins involved in KEGG metabolic pathways.

<p>Secreted proteins involved in KEGG metabolic pathways.</p

Venn diagram showing orthologues among the <i>H</i>. <i>oryzae</i>, <i>M</i>. <i>oryzae</i>, <i>G</i>. <i>graminis</i>, and <i>M</i>. <i>poae</i> secretomes.

<p>The values indicate the counts of the orthologue groups.</p

The 25 most abundant PFAM domains in the four secretomes.

<p>The 25 most abundant PFAM domains in the four secretomes.</p

The analysis pipeline applied to the <i>H</i>. <i>oryzae</i>, <i>M</i>. <i>oryzae</i>, <i>G</i>. <i>graminis</i>, and <i>M</i>. <i>poae</i> secretomes.

<p>The pipeline can be divided in three main steps: 1) secretome prediction, 2) functional analysis, and 3) expression analysis.</p