Developing guidelines for the human-wildlife interactions in conservation translocations

Workshop: Conservation translocation is a widely used management intervention to restore locally extinct or augment severely depleted species. Human dimension issues that influence the achievement of these aims are encountered at five different stages of the project life cycle: 1) Planning, 2) Initiation, 3) Implementation, 4) Ending, and 5) Post-exit stage. Overlooking such dimension may jeopardise the success of the project. Understanding and addressing human-wildlife interaction issues improve community involvement, peers’ acceptance and the support from various interest groups. In this workshop we propose to discuss participants’ experiences in human dimensions related to each of the 5 stages of a project’s life cycle.  Discussions aims to expand on findings from the IUCN/SSC CTSG HWIWG 2022 Guidelines to Facilitate Human-Wildlife Interactions in Conservation Translocations, to identify best practice and key issues for each stage to inform planning and promote wildlife conservation, collaboration amongst groups and coexistence

Facilitating human-wildlife interactions in conservation translocations

Workshop: Species reintroductions and translocations are widely used management interventions to restore locally extinct or augment severely depleted species. In such projects, the human dimension issues that influence reintroductions and translocations success are encountered at five different stages of the project life cycle: 1) The pre-project phase 2) At the start, 3) During implementation 4) At the end of the project and 5) Post-project. Whenever any of these are overlooked or treated lightly the result may jeopardise the success of the reintroduction/translocation project. Investments in human-dimension aspects improve community involvement, peers’ acceptance and the support from various interest groups. The Human-Wildlife Interactions Working Group (HWIWG) was formed in 2018 by members of the IUCN/SSC Conservation Translocation Specialist Group (CTSG). The group has facilitated online discussions and webinars with practitioners, researchers and academics from across the globe, on a wide variety of topics concerning human dimensions of reintroductions. In this workshop we propose to discuss with participants their experiences in human dimensions of conservation translocations in relation to each of 5 proposed stages of a project’s life cycle.  Discussions will be guided by findings from the HWIWG so that participants may identify best practice and key issues in considering human-dimension in each of these 5 stages to inform planning and promote conservation, collaboration amongst groups and coexistence

Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images

Multi-sequence of cardiac magnetic resonance (CMR) images can provide complementary information for myocardial pathology (scar and edema). However, it is still challenging to fuse these underlying information for pathology segmentation effectively. This work presents an automatic cascade pathology segmentation framework based on multi-modality CMR images. It mainly consists of two neural networks: an anatomical structure segmentation network (ASSN) and a pathological region segmentation network (PRSN). Specifically, the ASSN aims to segment the anatomical structure where the pathology may exist, and it can provide a spatial prior for the pathological region segmentation. In addition, we integrate a denoising auto-encoder (DAE) into the ASSN to generate segmentation results with plausible shapes. The PRSN is designed to segment pathological region based on the result of ASSN, in which a fusion block based on channel attention is proposed to better aggregate multi-modality information from multi-modality CMR images. Experiments from the MyoPS2020 challenge dataset show that our framework can achieve promising performance for myocardial scar and edema segmentation.Comment: 12 pages,MyoPS 202

Nonrepetitive Colouring via Entropy Compression

A vertex colouring of a graph is \emph{nonrepetitive} if there is no path whose first half receives the same sequence of colours as the second half. A graph is nonrepetitively $k$-choosable if given lists of at least $k$ colours at each vertex, there is a nonrepetitive colouring such that each vertex is coloured from its own list. It is known that every graph with maximum degree $\Delta$ is $c\Delta^2$-choosable, for some constant $c$. We prove this result with $c=1$ (ignoring lower order terms). We then prove that every subdivision of a graph with sufficiently many division vertices per edge is nonrepetitively 5-choosable. The proofs of both these results are based on the Moser-Tardos entropy-compression method, and a recent extension by Grytczuk, Kozik and Micek for the nonrepetitive choosability of paths. Finally, we prove that every graph with pathwidth $k$ is nonrepetitively $O(k^{2})$-colourable.Comment: v4: Minor changes made following helpful comments by the referee

Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images

Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction, such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired from two separate imaging centres. A consensus ground truth was obtained for all data using maximum likelihood estimation. Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus ground truth than most of the n-SD fixed-thresholding methods, with the exception of the FullWidth-at-Half-Maximum (FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution of this work, can be used to test and benchmark future algorithms that detect and quantify infarct in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly available through the website: https://www.cardiacatlas.org/web/guest/challenges

Rapid automatic segmentation of abnormal tissue in late gadolinium enhancement cardiovascular magnetic resonance images for improved management of long-standing persistent atrial fibrillation

Background: Atrial fibrillation (AF) is the most common heart rhythm disorder. In order for late Gd enhancement cardiovascular magnetic resonance (LGE CMR) to ameliorate the AF management, the ready availability of the accurate enhancement segmentation is required. However, the computer-aided segmentation of enhancement in LGE CMR of AF is still an open question. Additionally, the number of centres that have reported successful application of LGE CMR to guide clinical AF strategies remains low, while the debate on LGE CMR’s diagnostic ability for AF still holds. The aim of this study is to propose a method that reliably distinguishes enhanced (abnormal) from non-enhanced (healthy) tissue within the left atrial wall of (pre-ablation and 3 months post-ablation) LGE CMR data-sets from long-standing persistent AF patients studied at our centre. Methods: Enhancement segmentation was achieved by employing thresholds benchmarked against the statistics of the whole left atrial blood-pool (LABP). The test-set cross-validation mechanism was applied to determine the input feature representation and algorithm that best predict enhancement threshold levels. Results: Global normalized intensity threshold levels T PRE = 1 1/4 and T POST = 1 5/8 were found to segment enhancement in data-sets acquired pre-ablation and at 3 months post-ablation, respectively. The segmentation results were corroborated by using visual inspection of LGE CMR brightness levels and one endocardial bipolar voltage map. The measured extent of pre-ablation fibrosis fell within the normal range for the specific arrhythmia phenotype. 3D volume renderings of segmented post-ablation enhancement emulated the expected ablation lesion patterns. By comparing our technique with other related approaches that proposed different threshold levels (although they also relied on reference regions from within the LABP) for segmenting enhancement in LGE CMR data-sets of AF patients, we illustrated that the cut-off levels employed by other centres may not be usable for clinical studies performed in our centre. Conclusions: The proposed technique has great potential for successful employment in the AF management within our centre. It provides a highly desirable validation of the LGE CMR technique for AF studies. Inter-centre differences in the CMR acquisition protocol and image analysis strategy inevitably impede the selection of a universally optimal algorithm for segmentation of enhancement in AF studies