Deep Medical Image Analysis From Low-Resource Shape Coding

Medical image analysis is crucial in modern healthcare, enabling the quantification and identification of anatomical structures to characterize disease processes, assess treatment effects, and personalize patient healthcare. However, conventional medical image analysis frameworks require substantial resources, including high-quality imaging, extensive expert annotations, and scalable computational power. These demands limit their reliability in low resource settings, where access to such resources is constrained. Addressing these challenges is essential to ensure that advanced medical imaging techniques can benefit diverse healthcare environments, ultimately improving global access to high-quality care. This thesis aims to develop a self-integrated framework to overcome the key limitations f low-resource medical image analysis. We categorize low-resource medical image analysis into three primary research areas: (1) Feature extraction from low-quality medical images, where we introduce a novel token-matching framework to mitigate pixel-level anatomical ambiguities. (2) Multi-task learning with limited annotations, where we propose a semisupervised framework to establish task correspondences with few-shot examples despite the scarcity of high-quality labels. (3) Concept discovery with lightweight computation, where we develop a prompt-learning framework that eliminates the need for extensive training or fine-tuning configurations. Our proposed techniques enable a range of downstream applications, including biomarker segmentation in echocardiograph images, deformable landmark tracking in ultrasound scans, and vision-language segmentation through visual prompt learning. By integrating deep learning frameworks with low-resource shape coding, this thesis advances accurate, robust, and highly personalized decision-making in medical imaging.</p

Climatic and Landscape Influences on Fire Regimes from 1984 to 2010 in the Western United States

<div><p>An improved understanding of the relative influences of climatic and landscape controls on multiple fire regime components is needed to enhance our understanding of modern fire regimes and how they will respond to future environmental change. To address this need, we analyzed the spatio-temporal patterns of fire occurrence, size, and severity of large fires (> 405 ha) in the western United States from 1984–2010. We assessed the associations of these fire regime components with environmental variables, including short-term climate anomalies, vegetation type, topography, and human influences, using boosted regression tree analysis. Results showed that large fire occurrence, size, and severity each exhibited distinctive spatial and spatio-temporal patterns, which were controlled by different sets of climate and landscape factors. Antecedent climate anomalies had the strongest influences on fire occurrence, resulting in the highest spatial synchrony. In contrast, climatic variability had weaker influences on fire size and severity and vegetation types were the most important environmental determinants of these fire regime components. Topography had moderately strong effects on both fire occurrence and severity, and human influence variables were most strongly associated with fire size. These results suggest a potential for the emergence of novel fire regimes due to the responses of fire regime components to multiple drivers at different spatial and temporal scales. Next-generation approaches for projecting future fire regimes should incorporate indirect climate effects on vegetation type changes as well as other landscape effects on multiple components of fire regimes.</p></div

Environmental variables summarized for each fire and used in the analysis.

<p>Mean and s.d. for environmental variables were calculated from all the fires.</p><p>Environmental variables summarized for each fire and used in the analysis.</p

Spatial distributions of (a) smoothed density of large fires (fires per million ha per year), (b) smoothed mean fire size (ha), (c) smoothed mean high severity fire size (ha), and (d) smoothed percent of high severity burning (%) in the western US.

<p>Non-burnable areas are displayed in white.</p

Relative influences of variables that explained greater than 5% of the variation and marginal effects (red trend lines within each bar) from boosted regression tree models for (a) large fire occurrence, (b) fire size, and (c) percent of high severity burning.

<p>Values are specified for truncated bars. Abbreviations of predictor variables and their corresponding full names are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140839#pone.0140839.t001" target="_blank">Table 1</a>.</p

Simplified versions of the first tree of (a) fire occurrence model (b) fire size model, and (c) percent of high severity burning model computed with the boosted regression tree algorithm.

<p>The first three splits of each tree are shown to illustrate the interactions between key variables. The splitting variable and its corresponding splitting value are shown in oval above the node (variable abbreviation and units are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140839#pone.0140839.t001" target="_blank">Table 1</a>). The values in the rectangles at the terminal nodes represent the mean prediction and number of the records in the terminal nodes (n). The total number of records in the terminal nodes equals 0.75 (the bag fraction of the BRT model) of the total number of fires. Abbreviations: P: relative probability of fire occurrence; MFS: mean fire size; PHS: percent of high severity burning.</p

Conceptual model of major factors affecting fire occurrence, size, and severity.

<p>Red lines: human influences; green lines: direct climate influences; black lines: indirect climate (vegetation) influences; blue lines: topographic influences. Bold text represents groups of variables included in the analysis. Non-bold text represents implicit relationships that were not directly analyzed.</p

Spline correlograms illustrating the non-parametric spatial covariance function and 95% confidence intervals (gray area) for (a) large fire occurrence; (b) fire size; and (c) percent of high severity burning.

<p>Spline correlograms illustrating the non-parametric spatial covariance function and 95% confidence intervals (gray area) for (a) large fire occurrence; (b) fire size; and (c) percent of high severity burning.</p

Table_2_Trichoderma-Induced Ethylene Responsive Factor MsERF105 Mediates Defense Responses in Malus sieversii.XLSX

Trichoderma can induce plant hormone signal pathways mediating plant defenses, resulting in broad-spectrum resistance to phytopathogens. Herein, Malus sieversii seedlings were treated with Trichoderma biofertilizer and/or Alternaria alternata f. sp. mali, and transcriptome analysis revealed significant differential expression. There was a high similarity between the transcriptome expression profiles of Trichoderma-induced and A. alternata-infected M. sieversii samples for genes related to jasmonic acid (JA), ethylene, and salicylic acid (SA) signaling pathways. Additionally, Trichoderma biofertilizer activated numerous disease-resistant genes (ERF, NAC, bHLH, and STK) and defense response genes (DRP, ABC, and HSP). Among transcription factors, members of the ERF family were the most differentially expressed (18 ERFs), indicating that they may be closely related to defense responses. Among ERFs, differential expression of MsERF105 was the most significant (upregulated 27.6-fold compared to controls). MsERF105 was heterologously expressed in PdPap poplar (Populus davidiana × Populus alba var. pyramidalis Louche), and following infection with A. alternata (Aal), transgenic PdPap-MsERF105s plants displayed lower malondialdehyde (downregulated 41.4%) and reactive oxygen species (ROSs) levels, and higher reductase activities, especially superoxide dismutase (SOD; upregulated 77.5% compared to PdPap-ROK2 plants). Furthermore, the lesion areas of PdPap-MsERF105s leaves were significantly smaller (0.2%) than those of PdPap-ROK2 leaves (∼26.0%), and the cell membrane integrity was superior for PdPap-MsERF105s leaves. Thus, MsERF105 enhanced the resistance of PaPap poplar to Aal, presumably because MsERF105 activates the expression of PR1 and PDF1.2. In conclusion, Trichoderma biofertilizer modulated the differential expression of numerous disease resistance genes and defense response genes in M. sieversii in response to pathogen attack, and MsERF105 played important roles in this process.</p

Table_1_Trichoderma-Induced Ethylene Responsive Factor MsERF105 Mediates Defense Responses in Malus sieversii.DOCX

Trichoderma can induce plant hormone signal pathways mediating plant defenses, resulting in broad-spectrum resistance to phytopathogens. Herein, Malus sieversii seedlings were treated with Trichoderma biofertilizer and/or Alternaria alternata f. sp. mali, and transcriptome analysis revealed significant differential expression. There was a high similarity between the transcriptome expression profiles of Trichoderma-induced and A. alternata-infected M. sieversii samples for genes related to jasmonic acid (JA), ethylene, and salicylic acid (SA) signaling pathways. Additionally, Trichoderma biofertilizer activated numerous disease-resistant genes (ERF, NAC, bHLH, and STK) and defense response genes (DRP, ABC, and HSP). Among transcription factors, members of the ERF family were the most differentially expressed (18 ERFs), indicating that they may be closely related to defense responses. Among ERFs, differential expression of MsERF105 was the most significant (upregulated 27.6-fold compared to controls). MsERF105 was heterologously expressed in PdPap poplar (Populus davidiana × Populus alba var. pyramidalis Louche), and following infection with A. alternata (Aal), transgenic PdPap-MsERF105s plants displayed lower malondialdehyde (downregulated 41.4%) and reactive oxygen species (ROSs) levels, and higher reductase activities, especially superoxide dismutase (SOD; upregulated 77.5% compared to PdPap-ROK2 plants). Furthermore, the lesion areas of PdPap-MsERF105s leaves were significantly smaller (0.2%) than those of PdPap-ROK2 leaves (∼26.0%), and the cell membrane integrity was superior for PdPap-MsERF105s leaves. Thus, MsERF105 enhanced the resistance of PaPap poplar to Aal, presumably because MsERF105 activates the expression of PR1 and PDF1.2. In conclusion, Trichoderma biofertilizer modulated the differential expression of numerous disease resistance genes and defense response genes in M. sieversii in response to pathogen attack, and MsERF105 played important roles in this process.</p