Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage

This paper presents a spatiotemporal deep learning approach for mouse behavioural classification in the home-cage. Using a series of dual-stream architectures with assorted modifications to increase performance, we introduce a novel feature sharing approach that jointly processes the streams at regular intervals throughout the network. To investigate the efficacy of this approach, models were evaluated by dissociating the streams and training/testing in the same rigorous manner as the main classifiers. Using an annotated, publicly available dataset of a singly-housed mice, we achieve prediction accuracy of 86.47% using an ensemble of a Inception-based network and an attention-based network, both of which utilize this feature sharing. We also demonstrate through ablation studies that for all models, the feature-sharing architectures consistently perform better than conventional ones having separate streams. The best performing models were further evaluated on other activity datasets, both mouse and human. Future work will investigate the effectiveness of feature sharing to behavioural classification in the unsupervised anomaly detection domain

Moving to 3D: relationships between coral planar area, surface area and volume.

Coral reefs are a valuable and vulnerable marine ecosystem. The structure of coral reefs influences their health and ability to fulfill ecosystem functions and services. However, monitoring reef corals largely relies on 1D or 2D estimates of coral cover and abundance that overlook change in ecologically significant aspects of the reefs because they do not incorporate vertical or volumetric information. This study explores the relationship between 2D and 3D metrics of coral size. We show that surface area and volume scale consistently with planar area, albeit with morphotype specific conversion parameters. We use a photogrammetric approach using open-source software to estimate the ability of photogrammetry to provide measurement estimates of corals in 3D. Technological developments have made photogrammetry a valid and practical technique for studying coral reefs. We anticipate that these techniques for moving coral research from 2D into 3D will facilitate answering ecological questions by incorporating the 3rd dimension into monitoring

Hybrid Modality Fusion of Planar Scintigrams and CT Topograms to Localize Sentinel Lymph Nodes in Breast Lymphoscintigraphy: Technical Description and Phantom Studies

Lymphoscintigraphy is a nuclear medicine procedure that is used to detect sentinel lymph nodes (SLNs). This project sought to investigate fusion of planar scintigrams with CT topograms as a means of improving the anatomic reference for the SLN localization. Heretofore, the most common lymphoscintigraphy localization method has been backlighting with a 57Co sheet source. Currently, the most precise method of localization through hybrid SPECT/CT increases the patient absorbed dose by a factor of 34 to 585 (depending on the specific CT technique factors) over the conventional 57Co backlighting. The new approach described herein also uses a SPECT/CT scanner, which provides mechanically aligned planar scintigram and CT topogram data sets, but only increases the dose by a factor of two over that from 57Co backlighting. Planar nuclear medicine image fusion with CT topograms has been proven feasible and offers a clinically suitable compromise between improved anatomic details and minimally increased radiation dose

Unsupervised detection of mouse behavioural anomalies using two-stream convolutional autoencoders

This paper explores the application of unsupervised learning to detecting anomalies in mouse video data. The two models presented in this paper are a dual stream, 3D convolutional autoencoder (with residual connections) and a dual stream, 2D convolutional autoencoder. The publicly available dataset used here contains twelve videos of a single home-caged mice alongside frame level annotations. Under the pretext that the autoencoder only sees normal events, the video data was handcrafted to treat each behaviour as a pseudo-anomaly thereby eliminating them from the others during training. The results are presented for one conspicuous behaviour (hang) and one inconspicuous behaviour (groom). The performance of these models is compared to a single stream autoencoder and a supervised learning model, which are both based on the custom CAE encoder. Both models are also tested on the CUHK Avenue dataset were found to perform as well as some state-of-the-art architectures

The Reference Image Database to Evaluate Response to Therapy in Lung Cancer (RIDER) Project: A Resource for the Development of Change‐Analysis Software

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110105/1/cptclpt2008161.pd

Professional development and research are being neglected: a commentary on the 2019 RCR radiologists’ supporting professional activities (SPA) survey

When the National Health Service (NHS) acquired a statutory duty of care for quality in 1998, clinical governance became a mandatory and intrinsic part of modern medicine. Defined as “a framework through which NHS organisations are accountable for continuously improving the quality of their services and safe-guarding high standards of care by creating an environment in which excellence in clinical care will flourish”,1 the vehicle for NHS consultants to enact clinical governance was supporting professional activity (SPA). All activities that underpin direct clinical care (DCC) are encouraged during SPA time, including professional development, research, audit, teaching, clinical management, appraisal, and job planning.2,3 Adequate time for SPAs alongside DCC is therefore crucial for NHS consultants to\ud maintain excellence in clinical care.3 The recently published Royal College of Radiologists (RCR) Survey on Radiologists’ SPA4 has demonstrated three recurring themes, which are widely recognised to be growing concerns for our specialty

Impact of the COVID-19 crisis on imaging in oncological trials

The unprecedented demand for hospital services during the SARS-CoV-2 (COVID-19) pandemic has dramatically reduced the capability for dealing with non-acute health needs, including cancer care [1, 2]. To alleviate burden on health care systems, including imaging and laboratory services, curtailment of non-COVID-19-related research activity has been necessary [3]. Measures that reduce hospital visits have been adopted to limit risk of infection and death, which is critical in a cancer population whose age and immunocompromised status increases their risk [4]. Imaging, however, requires hospital visits and close contact with staff and equipment; both are sources of disease transmission. Equipment used to image COVID-19 cases may retain virus on its surface for days [5, 6] unless disinfected. The need for social distancing and for disinfecting equipment substantially slows imaging workflow and reduces throughput. This article discusses the specific impact of pandemics such as SARS-CoV-2 on imaging in oncological trials

Integrated Pharmacodynamic Analysis Identifies Two Metabolic Adaption Pathways to Metformin in Breast Cancer.

Late-phase clinical trials investigating metformin as a cancer therapy are underway. However, there remains controversy as to the mode of action of metformin in tumors at clinical doses. We conducted a clinical study integrating measurement of markers of systemic metabolism, dynamic FDG-PET-CT, transcriptomics, and metabolomics at paired time points to profile the bioactivity of metformin in primary breast cancer. We show metformin reduces the levels of mitochondrial metabolites, activates multiple mitochondrial metabolic pathways, and increases 18-FDG flux in tumors. Two tumor groups are identified with distinct metabolic responses, an OXPHOS transcriptional response (OTR) group for which there is an increase in OXPHOS gene transcription and an FDG response group with increased 18-FDG uptake. Increase in proliferation, as measured by a validated proliferation signature, suggested that patients in the OTR group were resistant to metformin treatment. We conclude that mitochondrial response to metformin in primary breast cancer may define anti-tumor effect

Use of modern imaging methods to facilitate trials of metastasis-directed therapy for oligometastatic disease in prostate cancer: a consensus recommendation from the EORTC Imaging Group

Oligometastatic disease represents a clinical and anatomical manifestation between localised and polymetastatic disease. In prostate cancer, as with other cancers, recognition of oligometastatic disease enables focal, metastasisdirected therapies. These therapies potentially shorten or postpone the use of systemic treatment and can delay further metastatic progression, thus increasing overall survival. Metastasis-directed therapies require imaging methods that definitively recognise oligometastatic disease to validate their efficacy and reliably monitor response, particularly so that morbidity associated with inappropriately treating disease subsequently recognised as polymetastatic can be avoided. In this Review, we assess imaging methods used to identify metastatic prostate cancer at first diagnosis, at biochemical recurrence, or at the castration-resistant stage. Standard imaging methods recommended by guidelines have insufficient diagnostic accuracy for reliably diagnosing oligometastatic disease. Modern imaging methods that use PET-CT with tumour-specific radiotracers (choline or prostate-specific membrane antigen ligand), and increasingly whole-body MRI with diffusion-weighted imaging, allow earlier and more precise identification of metastases. The European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group suggests clinical algorithms to integrate modern imaging methods into the care pathway at the various stages of prostate cancer to identify oligometastatic disease. The EORTC proposes clinical trials that use modern imaging methods to evaluate the benefits of metastasis-directed therapies

Twenty years on: RECIST as a biomarker of response in solid tumours. An EORTC Imaging Group – ESOI joint paper

Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for reponse classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required