5 research outputs found
Multimodal image-guided prostate fusion biopsy based on automatic deformable registration.
No full textTransrectal ultrasound (TRUS)-guided random prostate biopsy is, in spite of its low sensitivity, the gold standard for the diagnosis of prostate cancer. The recent advent of PET imaging using a novel dedicated radiotracer, [Formula: see text]-labeled prostate-specific membrane antigen (PSMA), combined with MRI provides improved pre-interventional identification of suspicious areas. This work proposes a multimodal fusion image-guided biopsy framework that combines PET-MRI images with TRUS, using automatic segmentation and registration, and offering real-time guidance.The prostate TRUS images are automatically segmented with a Hough transform-based random forest approach. The registration is based on the Coherent Point Drift algorithm to align surfaces elastically and to propagate the deformation field calculated from thin-plate splines to the whole gland.The method, which has minimal requirements and temporal overhead in the existing clinical workflow, is evaluated in terms of surface distance and landmark registration error with respect to the clinical ground truth. Evaluations on agar-gelatin phantoms and clinical data of 13 patients confirm the validity of this approach.The system is able to successfully map suspicious regions from PET/MRI to the interventional TRUS image
Privacy-Preserving Federated Brain Tumour Segmentation
No full textDue to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake. This poses challenges for training machine learning algorithms, such as deep convolutional networks, which often require large numbers of diverse training examples. Federated learning sidesteps this difficulty by bringing code to the patient data owners and only sharing intermediate model training updates among them. Although a high-accuracy model could be achieved by appropriately aggregating these model updates, the model shared could indirectly leak the local training examples. In this paper, we investigate the feasibility of applying differential-privacy techniques to protect the patient data in a federated learning setup. We implement and evaluate practical federated learning systems for brain tumour segmentation on the BraTS dataset. The experimental results show that there is a trade-off between model performance and privacy protection costs.</p
Snke OS 3D Lung CT Segmentation Challenge
No full textThis is the structured challenge design document for the "Snke OS 3D Lung CT Segmentation Challenge". More details can be found on the challenge's website. The structured design was introduced by the Biomedical Image Analysis ChallengeS (BIAS) initiative.
Background: Since the outbreak of the global Covid19 pandemic, the number of confirmed COVID-19 cases has reached over 16 million globally [1, 2], affecting virtually every territory, and with a fatality rate ~2-3% among the cohort of PCR-positive cases. Given the high demand for effective diagnosis and treatment of cases, the WHO recently released a rapid advice guide in July 2020 [3], in which chest imaging is conditionally recommended for several purposes, e.g. to aid diagnosis in the absence/delay of PCR testing, to assess the need for ICU admission and to inform the therapeutic management of patients.
Purpose: In this challenge, we aim to aid radiologists and physicians through objective and quantitative computational assessment of chest imaging in the context of COVID-19. We provide access to a large dataset of 3D chest CT imaging of the lung, collected from several European and international radiological centers. We call the international research community to develop and test artificial intelligence algorithms on this dataset.
Dataset: We provide access to low-dose chest CT imaging volumes from a mixed cohort of COVID-19 and non- COVID-19 cases. The dataset contains 113 labeled/segmented cases (79 COVID-19, 34 non-COVID-19), and >100 unlabeled volumes. A particular scientific challenge will lie in the effective use of unlabeled data through semi- and self-supervised training techniques. Labels represent five lung lobes and two lesions types, consolidation and ground-glass opacities. Labels are provided in a multi-hot encoding to allow region overlaps (e.g. lesions within lung lobes). For local development, we provide a realistic toy dataset of 96 synthetic volumes with 4D labelmaps.
Infrastructure: To maintain privacy, the anonymized imaging data remains non-disclosed within a biobank. Participating teams can design their algorithms locally using the representative synthetic dataset. Once ready, teams can submit training and validation jobs on the real dataset through Eisen, a deep learning framework based on pyTorch. Models are trained in the cloud by sponsorship of AWS. We actively promote open science, and require all participating teams to provide their solutions open-source to the technical and medical research community.
Participation: You can participate in two ways.
Hunters: Participate as a team with a maximum of 3 members as a competing team in the challenge. The incentive to the hunters: AWS cloud credits worth 7,500 EUR.
Rangers: Participate individually or in a team to help solve the Covid-19 challenge. You can submit tutorials, code or any educational material that is useful for the challenge. The incentive to the rangers: TBA.
Requirements: After the registration, there will be a “micro challenge” with the task of segmentation based on our synthetic toy dataset, for all teams in order to qualify for the main task.
References
[1] Bell, D.J. COVID-19. https://radiopaedia.org/articles/covid-19-4
[2] ACR. ACR Recommendations for the use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection. https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection
[3] WHO - Radiation and health. Use of chest imaging in COVID-19. https://www.who.int/publications/i/item/useof-chest-imaging-in-covid-19
UPDATES
1st september 2020: Updated the schedul
The future of digital health with federated learning
No full textData-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.</p
TAPIR: Transformers for Action, Phase, Instrument, and steps Recognition
No full textSurgical workflow analysis aims to improve the safety, planning, and efficiency of surgical procedures. However, most benchmarks for studying surgical interventions focus on a specific challenge instead of leveraging the intrinsic complementarity among different tasks. In this work, we present a new model to approach a holistic surgical scene understanding. Jointly with the release of the Phase, Step, Instrument, and Atomic Visual Action recognition (PSI-AVA) dataset by the Biomedical Computer Vision (BCV) group from the Universidad de Los Andes, we present Transformers for Action, Phase, Instrument, and steps Recognition (TAPIR) as a solid approach to surgical scene understanding. PSI-AVA includes annotations for both longterm (Phase and Step recognition) and short-term reasoning (Instrument detection and novel Atomic Action recognition) in robot-assisted radical prostatectomies videos. TAPIR leverages the dataset's multi-level annotations as it benefits from the learned representation on the instrument detection task to improve its classification capacity. Lastly, our experimental results in both PSI-AVA and other publicly available databases demonstrate that TAPIR is a stepping stone for future research in the holistic benchmark.Magíster en Ingeniería BiomédicaMaestrí
