38 research outputs found
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty
The BraTS dataset contains a mixture of high-grade and low-grade gliomas,
which have a rather different appearance: previous studies have shown that
performance can be improved by separated training on low-grade gliomas (LGGs)
and high-grade gliomas (HGGs), but in practice this information is not
available at test time to decide which model to use. By contrast with HGGs,
LGGs often present no sharp boundary between the tumor core and the surrounding
edema, but rather a gradual reduction of tumor-cell density.
Utilizing our 3D-to-2D fully convolutional architecture, DeepSCAN, which
ranked highly in the 2019 BraTS challenge and was trained using an
uncertainty-aware loss, we separate cases into those with a confidently
segmented core, and those with a vaguely segmented or missing core. Since by
assumption every tumor has a core, we reduce the threshold for classification
of core tissue in those cases where the core, as segmented by the classifier,
is vaguely defined or missing.
We then predict survival of high-grade glioma patients using a fusion of
linear regression and random forest classification, based on age, number of
distinct tumor components, and number of distinct tumor cores.
We present results on the validation dataset of the Multimodal Brain Tumor
Segmentation Challenge 2020 (segmentation and uncertainty challenge), and on
the testing set, where the method achieved 4th place in Segmentation, 1st place
in uncertainty estimation, and 1st place in Survival prediction.Comment: Presented (virtually) in the MICCAI Brainles workshop 2020. Accepted
for publication in Brainles proceeding
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge
Training a deep neural network is an optimization problem with four main
ingredients: the design of the deep neural network, the per-sample loss
function, the population loss function, and the optimizer. However, methods
developed to compete in recent BraTS challenges tend to focus only on the
design of deep neural network architectures, while paying less attention to the
three other aspects. In this paper, we experimented with adopting the opposite
approach. We stuck to a generic and state-of-the-art 3D U-Net architecture and
experimented with a non-standard per-sample loss function, the generalized
Wasserstein Dice loss, a non-standard population loss function, corresponding
to distributionally robust optimization, and a non-standard optimizer, Ranger.
Those variations were selected specifically for the problem of multi-class
brain tumor segmentation. The generalized Wasserstein Dice loss is a per-sample
loss function that allows taking advantage of the hierarchical structure of the
tumor regions labeled in BraTS. Distributionally robust optimization is a
generalization of empirical risk minimization that accounts for the presence of
underrepresented subdomains in the training dataset. Ranger is a generalization
of the widely used Adam optimizer that is more stable with small batch size and
noisy labels. We found that each of those variations of the optimization of
deep neural networks for brain tumor segmentation leads to improvements in
terms of Dice scores and Hausdorff distances. With an ensemble of three deep
neural networks trained with various optimization procedures, we achieved
promising results on the validation dataset of the BraTS 2020 challenge. Our
ensemble ranked fourth out of the 693 registered teams for the segmentation
task of the BraTS 2020 challenge.Comment: MICCAI 2020 BrainLes Workshop. Our method ranked fourth out of the
693 registered teams for the segmentation task of the BraTS 2020 challenge.
v2: Added some clarifications following reviewers' feedback (camera-ready
version
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the networks soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumors, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
The full acceptance of Deep Learning (DL) models in the clinical field is
rather low with respect to the quantity of high-performing solutions reported
in the literature. Particularly, end users are reluctant to rely on the rough
predictions of DL models. Uncertainty quantification methods have been proposed
in the literature as a potential response to reduce the rough decision provided
by the DL black box and thus increase the interpretability and the
acceptability of the result by the final user. In this review, we propose an
overview of the existing methods to quantify uncertainty associated to DL
predictions. We focus on applications to medical image analysis, which present
specific challenges due to the high dimensionality of images and their quality
variability, as well as constraints associated to real-life clinical routine.
We then discuss the evaluation protocols to validate the relevance of
uncertainty estimates. Finally, we highlight the open challenges of uncertainty
quantification in the medical field