Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration

The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations. This work describes a fully 3D prototypical few-shot segmentation algorithm, such that the trained networks can be effectively adapted to clinically interesting structures that are absent in training, using only a few labelled images from a different institute. First, to compensate for the widely recognised spatial variability between institutions in episodic adaptation of novel classes, a novel spatial registration mechanism is integrated into prototypical learning, consisting of a segmentation head and an spatial alignment module. Second, to assist the training with observed imperfect alignment, support mask conditioning module is proposed to further utilise the annotation available from the support images. Extensive experiments are presented in an application of segmenting eight anatomical structures important for interventional planning, using a data set of 589 pelvic T2-weighted MR images, acquired at seven institutes. The results demonstrate the efficacy in each of the 3D formulation, the spatial registration, and the support mask conditioning, all of which made positive contributions independently or collectively. Compared with the previously proposed 2D alternatives, the few-shot segmentation performance was improved with statistical significance, regardless whether the support data come from the same or different institutes.Comment: accepted by Medical Image Analysi

Semi-weakly-supervised neural network training for medical image registration

For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupervised losses are unavailable or ineffective. This correspondence-informing supervision entails cost in annotation that requires significant specialised effort. This paper describes a semi-weakly-supervised registration pipeline that improves the model performance, when only a small corresponding-ROI-labelled dataset is available, by exploiting unlabelled image pairs. We examine two types of augmentation methods by perturbation on network weights and image resampling, such that consistency-based unsupervised losses can be applied on unlabelled data. The novel WarpDDF and RegCut approaches are proposed to allow commutative perturbation between an image pair and the predicted spatial transformation (i.e. respective input and output of registration networks), distinct from existing perturbation methods for classification or segmentation. Experiments using 589 male pelvic MR images, labelled with eight anatomical ROIs, show the improvement in registration performance and the ablated contributions from the individual strategies. Furthermore, this study attempts to construct one of the first computational atlases for pelvic structures, enabled by registering inter-subject MRs, and quantifies the significant differences due to the proposed semi-weak supervision with a discussion on the potential clinical use of example atlas-derived statistics

Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration

The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations. This work describes a fully 3D prototypical few-shot segmentation algorithm, such that the trained networks can be effectively adapted to clinically interesting structures that are absent in training, using only a few labelled images from a different institute. First, to compensate for the widely recognised spatial variability between institutions in episodic adaptation of novel classes, a novel spatial registration mechanism is integrated into prototypical learning, consisting of a segmentation head and an spatial alignment module. Second, to assist the training with observed imperfect alignment, support mask conditioning module is proposed to further utilise the annotation available from the support images. Extensive experiments are presented in an application of segmenting eight anatomical structures important for interventional planning, using a data set of 589 pelvic T2-weighted MR images, acquired at seven institutes. The results demonstrate the efficacy in each of the 3D formulation, the spatial registration, and the support mask conditioning, all of which made positive contributions independently or collectively. Compared with the previously proposed 2D alternatives, the few-shot segmentation performance was improved with statistical significance, regardless whether the support data come from the same or different institutes

Stormwater Analysis and Water Quality Assessment of Urban Areas

4400011482, PIT WO 14Salt is widely used for road deicing purpose in winter, and salt application could raise stream chloride level and leads to deterioration of water quality. This study represents the first steps toward developing a comprehensive understanding of how the streams chloride levels are impacted by the salt operation. Toward this goal, this study developed a procedure for the flow path modeling of urban watersheds and applied it to two sites in Pittsburgh, PA, which are potentially susceptible to road salt application by PennDOT. The procedure was used in identifying areas contributing flows to PennDOT right-of-way, and vice versa. This study further took stream water quality samples during non-winter months for establishing baselines and during the winters of 2017 and 2018. Results show that over the non-winter months, the baseline stream chloride concentration has already exceeded criteria continuous concentration most of the time, but lies below the criteria maximum concentration of the environmental regulation. Test results on winter samples show that stream chloride concentration has risen following salt application after snow events, and has exceeded the criteria maximum concentration. The study also shows how surface model of different detail levels would affect the identified contributing areas related to target watersheds, and the importance of properly incorporating roadway features such as curves and bridges

A Study of Agricultural Watershed Health and Sustainability Using a New Catchment-scale Hydro-biogeochemical Model

Rainfall runoff and leaching are the main driving forces that lead the agricultural pollutants into streams, lakes and groundwater, and thereby deteriorate water quality. To make better management plan and achieve sustainable development within a watershed, it is crucial to study nutrient biogeochemical processes and their transport at a watershed scale where impacts of temporal and spatial heterogeneities are properly accounted for. However, current environmental models are unsatisfactory in modeling complex biogeochemical processes like denitrification at watershed scale due to various limitations. In this study, a new distributed hydro-biogeochemical model, DHSVM-N, is developed based on the widely used fully distributed and physically-based hydrological model DHSVM (Distributed Hydrology Soil and Vegetation Model), and the SWAT model (Soil & Water Assessment Tool). DHSVM-N is then used to investigate longstanding and challenging watershed health issues caused by agricultural nutrient contaminants. Compared to the original DHSVM model, DHSVM-N includes four main features: (1) dynamic vegetation growth, (2) vegetation-soil nutrient cycle, (3) contaminant transport module for nitrogen, and (4) wetland and agricultural management simulation. The construct of DHSVM-N model endows it to incorporate realistic landscape unit connectivity in routing/transport processes for overland flow and channel flow in giving physically-based flow pathways and captures spatial distributions of soil moisture. This new DHSVM-N model is applied to a rural watershed to investigate impacts of different model transport approaches on simulating nitrogen-related process and spatial patterns of denitrification under different scenarios, and to evaluate impacts of agricultural management activities on watershed’s health by considering different placement of treatment facilities and different locations of pollution sources. Results obtained highlight the importance of hydrological processes in modeling biogeochemical processes and the critical role of pollutant transport method in adequately simulating biogeochemical hot spots. This work also showcases how watershed responds to different placement designs of Best Management Practices (BMPs) where wetland with optimized placement can contribute to reduction in nitrate export for nonpoint source and unidentified point source respectively and increase in system reliability. The result emphasizes the need to properly consider BMPs locations when making field-scale management plans and when conducting the watershed health and sustainability study

Transcriptome Analysis of Low-Temperature-Treated Tetraploid Yellow Actinidia chinensis Planch. Tissue Culture Plantlets

The cold-resistant mechanism of yellow kiwifruit associated with gene regulation is poorly investigated. In this study, to provide insight into the causes of differences in low-temperature tolerance and to better understand cold-adaptive mechanisms, we treated yellow tetraploid kiwifruit ‘SWFU03’ tissue culture plantlets at low temperatures, used these plantlets for transcriptome analysis, and validated the expression levels of ten selected genes by real-time quantitative polymerase chain reaction (RT-qPCR) analysis. A number of 1630 differentially expressed genes (DEGs) were identified, of which 619 pathway genes were up-regulated, and 1011 were down-regulated in the cold treatment group. The DEGs enriched in the cold tolerance-related pathways mainly included the plant hormone signal transduction and the starch and sucrose metabolism pathway. RT-qPCR analysis confirmed the expression levels of eight up-regulated genes in these pathways in the cold-resistant mutants. In this study, cold tolerance-related pathways (the plant hormone signal transduction and starch and sucrose metabolism pathway) and genes, e.g., CEY00_Acc03316 (abscisic acid receptor PYL), CEY00_Acc13130 (bZIP transcription factor), CEY00_Acc33627 (TIFY protein), CEY00_Acc26744 (alpha-trehalose-phosphate synthase), CEY00_Acc28966 (beta-amylase), CEY00_Acc16756 (trehalose phosphatase), and CEY00_Acc08918 (beta-amylase 4) were found