Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation

In large studies involving multi protocol Magnetic Resonance Imaging (MRI), it can occur to miss one or more sub-modalities for a given patient owing to poor quality (e.g. imaging artifacts), failed acquisitions, or hallway interrupted imaging examinations. In some cases, certain protocols are unavailable due to limited scan time or to retrospectively harmonise the imaging protocols of two independent studies. Missing image modalities pose a challenge to segmentation frameworks as complementary information contributed by the missing scans is then lost. In this paper, we propose a novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. Instead of designing one network for each possible subset of present sub-modalities or using frameworks to mix feature maps, missing data can be generated from a single model based on all the available samples. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing. Our experiments against competitive segmentation baselines with missing sub-modality on BraTS'19 dataset indicate the effectiveness of the MGP-VAE model for segmentation tasks.Comment: Accepted in MICCAI 202

Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation

Detection of tumors in metastatic colorectal cancer (mCRC) plays an essential role in the early diagnosis and treatment of liver cancer. Deep learning models backboned by fully convolutional neural networks (FCNNs) have become the dominant model for segmenting 3D computerized tomography (CT) scans. However, since their convolution layers suffer from limited kernel size, they are not able to capture long-range dependencies and global context. To tackle this restriction, vision transformers have been introduced to solve FCNN's locality of receptive fields. Although transformers can capture long-range features, their segmentation performance decreases with various tumor sizes due to the model sensitivity to the input patch size. While finding an optimal patch size improves the performance of vision transformer-based models on segmentation tasks, it is a time-consuming and challenging procedure. This paper proposes a technique to select the vision transformer's optimal input multi-resolution image patch size based on the average volume size of metastasis lesions. We further validated our suggested framework using a transfer-learning technique, demonstrating that the highest Dice similarity coefficient (DSC) performance was obtained by pre-training on training data with a larger tumour volume using the suggested ideal patch size and then training with a smaller one. We experimentally evaluate this idea through pre-training our model on a multi-resolution public dataset. Our model showed consistent and improved results when applied to our private multi-resolution mCRC dataset with a smaller average tumor volume. This study lays the groundwork for optimizing semantic segmentation of small objects using vision transformers. The implementation source code is available at:https://github.com/Ramtin-Mojtahedi/OVTPS

Parameter-Efficient Methods for Metastases Detection from Clinical Notes

Understanding the progression of cancer is crucial for defining treatments for patients. The objective of this study is to automate the detection of metastatic liver disease from free-style computed tomography (CT) radiology reports. Our research demonstrates that transferring knowledge using three approaches can improve model performance. First, we utilize generic language models (LMs), pretrained in a self-supervised manner. Second, we use a semi-supervised approach to train our model by automatically annotating a large unlabeled dataset; this approach substantially enhances the model's performance. Finally, we transfer knowledge from related tasks by designing a multi-task transfer learning methodology. We leverage the recent advancement of parameter-efficient LM adaptation strategies to improve performance and training efficiency. Our dataset consists of CT reports collected at Memorial Sloan Kettering Cancer Center (MSKCC) over the course of 12 years. 2,641 reports were manually annotated by domain experts; among them, 841 reports have been annotated for the presence of liver metastases. Our best model achieved an F1-score of 73.8%, a precision of 84%, and a recall of 65.8%.Comment: 6 pages, 1 figure, The 36th Canadian Conference on Artificial Intelligenc

Sensitivity analysis and automation for intraoperative implementation of the atlas-based method for brain shift correction

ABSTRACT The use of biomechanical models to correct the misregistration due to deformation in image guided neurosurgical systems has been a growing area of investigation. In previous work, an atlas-based inverse model was developed to account for soft-tissue deformations during image-guided surgery. Central to that methodology is a considerable amount of pre-computation and planning. The goal of this work is to evaluate techniques that could potentially reduce that burden. Distinct from previous manual techniques, an automated segmentation technique is described for the cerebrum and dural septa. The shift correction results using this automated segmentation method were compared to those using the manual methods. In addition, the extent and distribution of the surgical parameters associated with the deformation atlas were investigated by a sensitivity analysis using simulation experiments and clinical data. The shift correction results did not change significantly using the automated method (correction of 73±13% ) as compared to the semi-automated method from previous work (correction of 76±13%). The results of the sensitivity analysis show that the atlas could be constructed by coarser sampling (six fold reduction) without substantial degradation in the shift reconstruction, a decrease in preoperative computational time from 13.1±3.5 hours to 2.2±0.6 hours. The automated segmentation technique and the findings of the sensitivity study have significant impact on the reduction of pre-operative computational time, improving the utility of the atlas-based method. The work in this paper suggests that the atlas-based technique can become a 'time of surgery' setup procedure rather than a pre-operative computing strategy

Spatial mapping of the collagen distribution in human and mouse tissues by force volume atomic force microscopy

Changes in the elastic properties of living tissues during normal development and in pathological processes are often due to modifications of the collagen component of the extracellular matrix at various length scales. Force volume AFM can precisely capture the mechanical properties of biological samples with force sensitivity and spatial resolution. The integration of AFM data with data of the molecular composition contributes to understanding the interplay between tissue biochemistry, organization and function. The detection of micrometer-size, heterogeneous domains at different elastic moduli in tissue sections by AFM has remained elusive so far, due to the lack of correlations with histological, optical and biochemical assessments. In this work, force volume AFM is used to identify collagen-enriched domains, naturally present in human and mouse tissues, by their elastic modulus. Collagen identification is obtained in a robust way and affordable timescales, through an optimal design of the sample preparation method and AFM parameters for faster scan with micrometer resolution. The choice of a separate reference sample stained for collagen allows correlating elastic modulus with collagen amount and position with high statistical significance. The proposed preparation method ensures safe handling of the tissue sections guarantees the preservation of their micromechanical characteristics over time and makes it much easier to perform correlation experiments with different biomarkers independently

Structural factors and best practices in implementing a linkage to HIV care program using the ARTAS model

<p>Abstract</p> <p>Background</p> <p>Implementation of linkage to HIV care programs in the U.S. is poorly described in the literature despite the central role of these programs in delivering clients from HIV testing facilities to clinical care sites. Models demonstrating success in linking clients to HIV care from testing locations that do not have co-located medical care are especially needed.</p> <p>Methods</p> <p>Data from the Antiretroviral Treatment Access Studies-II project ('ARTAS-II') as well as site visit and project director reports were used to describe structural factors and best practices found in successful linkage to care programs. Successful programs were able to identify recently diagnosed HIV-positive persons and ensure that a high percentage of persons attended an initial HIV primary care provider visit within six months of enrolling in the linkage program.</p> <p>Results</p> <p>Eight categories of best practices are described, supplemented by examples from 5 of 10 ARTAS-II sites. These five sites highlighted in the best practices enrolled a total of 352 HIV+ clients and averaged 85% linked to care after six months. The other five grantees enrolled 274 clients and averaged 72% linked to care after six months. Sites with co-located HIV primary medical care services had higher linkage to care rates than non-co-located sites (87% vs. 73%). Five grantees continued linkage to care activities in some capacity after project funding ended.</p> <p>Conclusions</p> <p>With the push to expand HIV testing in all U.S. communities, implementation and evaluation of linkage to care programs is needed to maximize the benefits of expanded HIV testing efforts</p

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community

Standards of lithium monitoring in mental health trusts in the UK

<p>Abstract</p> <p>Background</p> <p>Lithium is a commonly prescribed drug with a narrow therapeutic index, and recognised adverse effects on the kidneys and thyroid. Clinical guidelines for the management of bipolar affective disorder published by The National Institute for Health and Clinical Excellence (NICE) recommend checks of renal and thyroid function before lithium is prescribed. They further recommend that all patients who are prescribed lithium should have their renal and thyroid function checked every six months, and their serum lithium checked every three months. Adherence to these recommendations has not been subject to national UK audit.</p> <p>Methods</p> <p>The Prescribing Observatory for Mental Health (POMH-UK) invited all National Health Service Mental Health Trusts in the UK to participate in a benchmarking audit of lithium monitoring against recommended standards. Data were collected retrospectively from clinical records and submitted electronically.</p> <p>Results</p> <p>436 clinical teams from 38 Trusts submitted data for 3,373 patients. In patients recently starting lithium, there was a documented baseline measure of renal or thyroid function in 84% and 82% respectively. For patients prescribed lithium for a year or more, the NICE standards for monitoring lithium serum levels, and renal and thyroid function were met in 30%, 55% and 50% of cases respectively.</p> <p>Conclusions</p> <p>The quality of lithium monitoring in patients who are in contact with mental health services falls short of recognised standards and targets. Findings from this audit, along with reports of harm received by the National Patient Safety Agency, prompted a Patient Safety Alert mandating primary care, mental health and acute Trusts, and laboratory staff to work together to ensure systems are in place to support recommended lithium monitoring by December 2010.</p

On the vegetation of Mosor

Im vorliegenden Beitrag wird ein Überblick über die Vegetation des Mosor-Gebirges, die sämtlich zu der mediterranen Region gehört, gegeben. Dies hängt von den klimatischen Verhältnissen bzw. von der geographischen Lage des Mosor-Gebirges, das gänzlich im Hintergrund des zentralen Teiles des mittleren immergrünen Gebietes Kroatiens verläuft, ab. Gewisse Pflanzengesellschaften und einige Pflanzenarten befinden sich hier auf der Nordwest- bzw. Südgrenze ihres Verbreitungsgebietes.Mosor se s obzirom na svoj fitogeografski položaj odlikuje nekim specifičnostima u biljnom pokrovu. Iako ima visinu od 1340 m/nm, vegetacija na Mosoru pripada u cijelosti mediteranskoj regiji. Šumska zajednica Carpinetum orientalis adriaticum zauzima ondje položaje od 400 do 900 m/nm, a zajednica Seslerio-Ostryetum od 900 m/nm naviše. Na obroncima Mosora zajednica Andropogoni-Diplachnetum serotinae dosiže, koliko je dosad poznato, najjužniju granicu svoje raširenosti. S druge strane, zajednica Erico-Cistetum cretici i Brachypodio-Trifolietum stellati imaju, prema dosadašnjim istraživanjima, na području Mosora i široj okolici Splita svoju sjevernu granicu raširenosti. Isto tako, po podacima iz literature, zajednica Campanulo-Moltkietum petraeae ima na Mosoru (uz Kozjak i Dinaru) svoju sjeverozapadnu granicu. Inače biljni pokrov Mosora, iako jako utjecajan, odlikuje se gotovo svim najznačajnijim tipovima vegetacije mediteranske regije.The papeir gives a short survey of the vegetational cover of Mosor, starting from climatozonal vegetation to the various stages of its degradation. In respect to its phytogeographic position, the mountain of Mosor has certain specific features in its vegetational cover. Although the mountain is 1340 m high, the vegetation of Mosor belongs entirely to the Mediterranean region. The forest community Carpinetum orientalis adriaticum is situated here at places between 400 to 900 m above sea, and the community Seslerio-Ostryetum from 900 m upwards. On the slopes of Mosor, the community Andropogoni-Diplachnetum reaches, as far as it is known today, the southernmost border of its distribution. On the other hand, the communities Erico-Cistetum cretici and Brachypodio-Trijolietum stellati reach, according to current investigations, their northern border in the area of Mosor and the wider surroundings of Split. Also, according to the literature, the community Campanulo-Moltkietum petraeae has its north-western border at Mosor (together with Kozjak and Dinara mts). Otherwise the vegetational cover of Mosor, although of great influence, is characterized by all the most significant types of the vegetation of the Mediterranean region

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts