Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations

Deep-learning has proved in recent years to be a powerful tool for image analysis and is now widely used to segment both 2D and 3D medical images. Deep-learning segmentation frameworks rely not only on the choice of network architecture but also on the choice of loss function. When the segmentation process targets rare observations, a severe class imbalance is likely to occur between candidate labels, thus resulting in sub-optimal performance. In order to mitigate this issue, strategies such as the weighted cross-entropy function, the sensitivity function or the Dice loss function, have been proposed. In this work, we investigate the behavior of these loss functions and their sensitivity to learning rate tuning in the presence of different rates of label imbalance across 2D and 3D segmentation tasks. We also propose to use the class re-balancing properties of the Generalized Dice overlap, a known metric for segmentation assessment, as a robust and accurate deep-learning loss function for unbalanced tasks

EXclusion of non-Involved uterus from the Target Volume (EXIT-trial): An individualized treatment for locally advanced cervical cancer using modern radiotherapy and imaging techniques

Background: Definitive chemoradiotherapy is standard of care in locally advanced cervical cancer (LACC). Both toxicity and local relapse remain major concerns in this treatment. We hypothesize that a magnetic resonance imaging (MRI) based redefining of the radiotherapeutic target volume will lead to a reduction of acute and late toxicity. In our center, chemoradiotherapy followed by hysterectomy was implemented successfully in the past. This enables us to assess the safety of reducing the target volume but also to explore the biological effects of chemoradiation on the resected hysterectomy specimen. Methods: The EXIT-trial is a phase II, single arm study aimed at LACC patients. This study evaluates whether a MRI-based exclusion of the non-tumor-bearing parts of the uterus out of the target volume results in absence of tumor in the non-high doses irradiated part of the uterus in the hysterectomy specimen. Secondary endpoints include a dosimetric comparison of dose on normal tissue when comparing study treatment plans compared to treatment of the whole uterus at high doses; acute and chronic toxicity, overall survival, local relapse- and progression-free survival. In the translational part of the study, we will evaluate the hypothesis that the baseline apparent diffusion coefficient (ADC) values of diffusion weighted MRI and its evolution 2 weeks after start of CRT, for the whole tumor as well as for intra-tumoral regions, is prognostic for residual tumor on the hysterectomy specimen. Discussion: Although MRI is already used to guide target delineation in brachytherapy, the EXIT-trial is the first to use this information to guide target delineation in external beam radiotherapy. Early therapy resistance prediction using DW-MRI opens a window for early treatment adaptation or further dose-escalation on tumors/intratumoral regions at risk for treatment failure

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets

Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these cases. In the meantime, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly. However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions. Developing a DNN for such a joint task is currently hampered by the fact that annotated datasets typically address only one specific task and rely on task-specific imaging protocols including a task-specific set of imaging modalities. In this work, we propose a novel approach to build a joint tissue and lesion segmentation model from aggregated task-specific hetero-modal domain-shifted and partially-annotated datasets. Starting from a variational formulation of the joint problem, we show how the expected risk can be decomposed and optimised empirically. We exploit an upper bound of the risk to deal with heterogeneous imaging modalities across datasets. To deal with potential domain shift, we integrated and tested three conventional techniques based on data augmentation, adversarial learning and pseudo-healthy generation. For each individual task, our joint approach reaches comparable performance to task-specific and fully-supervised models. The proposed framework is assessed on two different types of brain lesions: White matter lesions and gliomas. In the latter case, lacking a joint ground-truth for quantitative assessment purposes, we propose and use a novel clinically-relevant qualitative assessment methodology.Comment: MIDL 2019 special issue - Medical Image Analysi

Effects of Stellate Ganglion Block on Analgesia Produced by Cervical Paravertebral Block as Established by Quantitative Sensory Testing: A Randomized Controlled Trial

Objective: To use quantitative sensory testing (QST) to assess whether a stellate ganglion block (SGB) modulates the analgesia induced by cervical paravertebral block (CPVB). Design: A prospective double-blind randomized controlled trial. Setting: Department of Anesthesia, Antwerp University Hospital, October 2011 to December 2015. Subjects: Twenty-eight adults scheduled for arthroscopy of a nonfractured shoulder were enrolled. Methods: Participants were randomly assigned to receive either single CPVB (5 mL of levobupivacaine 0.5%) or combined CPVB + SGB (5 mL and 3 mL of levobubivacaine 0.5%, respectively). The detection thresholds for cold/warm sensations and cold/heat pain were established using thermal QST on the C4-C7 dermatomes before local anesthetic infiltration and at 0.5, 6, 10, and 24 hours thereafter. Our primary outcome was the time course of QST thresholds for the different neurosensitive/nociceptive modalities. As secondary and tertiary outcomes, we evaluated the degree of motor block and the time to first administration of rescue analgesics. Results: We randomized 20 patients. There were no significant differences in the detection thresholds for the neurosensitive/nociceptive modalities, motor block, or timing for rescue analgesics between the groups (P = 0.15-0.94). All patients with CPVB + SGB exhibited Horner's signs, whereas patients in the CPVB group did not exhibit these signs; however, this does not exclude sympathetic block. Conclusions: We were unable to demonstrate any analgesic benefit of CPVB + SGB in arthroscopic shoulder surgery. It is therefore not unreasonable to suppose that pain from soft tissue injuries without bony lesions is transmitted mainly by somatic nerves with no or only minimal involvement of the sympathetic nervous system.status: publishe

3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects

Extremely small objects (ESO) have become observable on clinical routine magnetic resonance imaging acquisitions, thanks to a reduction in acquisition time at higher resolution. Despite their small size (usually <10 voxels per object for an image of more than 106 voxels), these markers reflect tissue damage and need to be accounted for to investigate the complete phenotype of complex pathological pathways. In addition to their very small size, variability in shape and appearance leads to high labelling variability across human raters, resulting in a very noisy gold standard. Such objects are notably present in the context of cerebral small vessel disease where enlarged perivascular spaces and lacunes, commonly observed in the ageing population, are thought to be associated with acceleration of cognitive decline and risk of dementia onset. In this work, we redesign the RCNN model to scale to 3D data, and to jointly detect and characterise these important markers of age-related neurovascular changes. We also propose training strategies enforcing the detection of extremely small objects, ensuring a tractable and stable training process

Comparison of supine or prone crawl photon or proton breast and regional lymph node radiation therapy including the internal mammary chain

Abstract We report on a dosimetrical study comparing supine (S) and prone-crawl (P) position for radiotherapy of whole breast (WB) and loco-regional lymph node regions, including the internal mammary chain (LN_IM). Six left sided breast cancer patients were CT-simulated in S and P positions and four patients only in P position. Treatment plans were made using non-coplanar volumetric modulated arc photon therapy (VMAT) or pencil beam scanning intensity modulated proton therapy (IMPT). Dose prescription was 15*2.67 Gy(GyRBE). The average mean heart doses for S or P VMAT were 5.6 or 4.3 Gy, respectively (p = 0.16) and 1.02 or 1.08 GyRBE, respectively for IMPT (p = 0.8; p < 0.001 for IMPT versus VMAT). The average mean lung doses for S or P VMAT were 5.91 or 2.90 Gy, respectively (p = 0.002) and 1.56 or 1.09 GyRBE, respectively for IMPT (p = 0.016). In high-risk patients, average (range) thirty-year mortality rates from radiotherapy-related cardiac injury and lung cancer were estimated at 6.8(5.4–9.4)% or 3.8(2.8–5.1)% for S or P VMAT (p < 0.001), respectively, and 1.6(1.1–2.0)% or 1.2(0.8–1.6)% for S or P IMPT (p = 0.25), respectively. Radiation-related mortality risk could outweigh the ~8% disease-specific survival benefit of WB + LN_IM radiotherapy for S VMAT but not P VMAT. IMPT carries the lowest radiation-related mortality risks