SAF-IS: a Spatial Annotation Free Framework for Instance Segmentation of Surgical Tools

Instance segmentation of surgical instruments is a long-standing research problem, crucial for the development of many applications for computer-assisted surgery. This problem is commonly tackled via fully-supervised training of deep learning models, requiring expensive pixel-level annotations to train. In this work, we develop a framework for instance segmentation not relying on spatial annotations for training. Instead, our solution only requires binary tool masks, obtainable using recent unsupervised approaches, and binary tool presence labels, freely obtainable in robot-assisted surgery. Based on the binary mask information, our solution learns to extract individual tool instances from single frames, and to encode each instance into a compact vector representation, capturing its semantic features. Such representations guide the automatic selection of a tiny number of instances (8 only in our experiments), displayed to a human operator for tool-type labelling. The gathered information is finally used to match each training instance with a binary tool presence label, providing an effective supervision signal to train a tool instance classifier. We validate our framework on the EndoVis 2017 and 2018 segmentation datasets. We provide results using binary masks obtained either by manual annotation or as predictions of an unsupervised binary segmentation model. The latter solution yields an instance segmentation approach completely free from spatial annotations, outperforming several state-of-the-art fully-supervised segmentation approaches

Atmosphere-ocean-ice interactions in the Amundsen Sea Embayment, West Antarctica

Over recent decades outlet glaciers of the Amundsen Sea Embayment (ASE), West Antarctica, have accelerated, thinned and retreated, and are now contributing approximately 10% to global sea level rise. All the ASE glaciers flow into ice shelves, and it is the thinning of these since the 1970s, and their ungrounding from “pinning points” that is widely held to be responsible for triggering the glaciers’ decline. These changes have been linked to the inflow of warm Circumpolar Deep Water (CDW) onto the ASE's continental shelf. CDW delivery is highly variable, and is closely related to the regional atmospheric circulation. The ASE is south of the Amundsen Sea Low (ASL), which has a large variability and which has deepened in recent decades. The ASL is influenced by the phase of the Southern Annular Mode, along with tropical climate variability. It is not currently possible to simulate such complex atmosphere-ocean-ice interactions in models, hampering prediction of future change. The current retreat could mark the beginning of an unstable phase of the ASE glaciers that, if continued, will result in collapse of the West Antarctic Ice Sheet, but numerical ice-sheet models currently lack the predictive power to answer this question. It is equally possible that the recent retreat will be short-lived and that the ASE will find a new stable state. Progress is hindered by incomplete knowledge of bed topography in the vicinity of the grounding line. Furthermore, a number of key processes are still missing or poorly represented in models of ice-flow

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)

Causes d'hyperthermie chez le malade atteint de cirrhose consultant aux urgences

PARIS7-Xavier Bichat (751182101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation

Automatic surgical instrument segmentation of endoscopic images is a crucial building block of many computer-assistance applications for minimally invasive surgery. So far, state-of-the-art approaches completely rely on the availability of a ground-truth supervision signal, obtained via manual annotation, thus expensive to collect at large scale. In this paper, we present FUN-SIS, a Fully-UNsupervised approach for binary Surgical Instrument Segmentation. FUN-SIS trains a per-frame segmentation model on completely unlabelled endoscopic videos, by solely relying on implicit motion information and instrument shape-priors. We define shape-priors as realistic segmentation masks of the instruments, not necessarily coming from the same dataset/domain as the videos. The shape-priors can be collected in various and convenient ways, such as recycling existing annotations from other datasets. We leverage them as part of a novel generative-adversarial approach, allowing to perform unsupervised instrument segmentation of optical-flow images during training. We then use the obtained instrument masks as pseudo-labels in order to train a per-frame segmentation model; to this aim, we develop a learning-from-noisy-labels architecture, designed to extract a clean supervision signal from these pseudo-labels, leveraging their peculiar noise properties. We validate the proposed contributions on three surgical datasets, including the MICCAI 2017 EndoVis Robotic Instrument Segmentation Challenge dataset. The obtained fully-unsupervised results for surgical instrument segmentation are almost on par with the ones of fully-supervised state-of-the-art approaches. This suggests the tremendous potential of the proposed method to leverage the great amount of unlabelled data produced in the context of minimally invasive surgery

A Kinematic Bottleneck Approach For Pose Regression of Flexible Surgical Instruments directly from Images

3-D pose estimation of instruments is a crucial step towards automatic scene understanding in robotic minimally invasive surgery. Although robotic systems can potentially directly provide joint values, this information is not commonly exploited inside the operating room, due to its possible unreliability, limited access and the time-consuming calibration required, especially for continuum robots. For this reason, standard approaches for 3-D pose estimation involve the use of external tracking systems. Recently, image-based methods have emerged as promising, non-invasive alternatives. While many image-based approaches in the literature have shown accurate results, they generally require either a complex iterative optimization for each processed image, making them unsuitable for real-time applications, or a large number of manually-annotated images for efficient learning. In this letter, we propose a self-supervised image-based method, exploiting, at training time only, the imprecise kinematic information provided by the robot. In order to avoid introducing time-consuming manual annotations, the problem is formulated as an auto-encoder, smartly bottlenecked by the presence of a physical model of the robotic instruments and surgical camera, forcing a separation between image background and kinematic content. Validation of the method was performed on semi-synthetic , phantom and in-vivo datasets, obtained using a flexible robotized endoscope, showing promising results for real-time image-based 3-D pose estimation of surgical instruments

Magnetophoretic manipulation in microsystem using carbonyl iron-polydimethylsiloxane microstructures

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Day 1 extracranial internal carotid artery patency is associated with good outcome after mechanical thrombectomy for tandem occlusion

International audienceBackground and Purpose- Optimal management of the extracranial occlusive component remains controversial in patients with acute ischemic stroke by tandem occlusion treated with mechanical thrombectomy. We investigated the association between extracranial internal carotid artery (ICA) patency at day 1 and the clinical outcome after mechanical thrombectomy. Methods- Consecutive patients with acute ischemic stroke with tandem occlusion were identified from a hospital-based prospective registry from 2011 to 2017. Baseline characteristics, angiographic outcomes, and day 1 ICA patency assessed by MR angiography were analyzed with regard to their associations with 3-month modified Rankin Scale scores. Favorable outcome was defined as a modified Rankin Scale score of 0 to 2 at 3 months. Results- Of 594 patients with acute ischemic stroke treated with mechanical thrombectomy during the study period, 83 met inclusion criteria. Successful recanalization (modified Thrombolysis in Cerebral Infarction, 2b/3) was achieved in 61.5%. Extracranial ICA was patent in 37 of 83 patients (44.6%) at day 1, more frequently in those with prior intravenous thrombolysis ( P=0.035) or with cervical revascularization procedure (balloon angioplasty or stenting, P=0.034). Favorable 3-month functional outcome was more frequent in patients with patent extracranial ICA at day 1 (adjusted odds ratio, 4.72; 95% CI, 1.76-13.34; P=0.003) independent of intracranial recanalization success. Conclusions- Day 1 stable extracranial ICA patency is associated with better clinical outcome in patients with acute ischemic stroke with tandem occlusions. Randomized studies are needed