330 research outputs found
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
A cascade of fully convolutional neural networks is proposed to segment
multi-modal Magnetic Resonance (MR) images with brain tumor into background and
three hierarchical regions: whole tumor, tumor core and enhancing tumor core.
The cascade is designed to decompose the multi-class segmentation problem into
a sequence of three binary segmentation problems according to the subregion
hierarchy. The whole tumor is segmented in the first step and the bounding box
of the result is used for the tumor core segmentation in the second step. The
enhancing tumor core is then segmented based on the bounding box of the tumor
core segmentation result. Our networks consist of multiple layers of
anisotropic and dilated convolution filters, and they are combined with
multi-view fusion to reduce false positives. Residual connections and
multi-scale predictions are employed in these networks to boost the
segmentation performance. Experiments with BraTS 2017 validation set show that
the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for
enhancing tumor core, whole tumor and tumor core, respectively. The
corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and
0.7748, respectively.Comment: 12 pages, 5 figures. MICCAI Brats Challenge 201
Detection-aided liver lesion segmentation using deep learning
A fully automatic technique for segmenting the liver and localizing its
unhealthy tissues is a convenient tool in order to diagnose hepatic diseases
and assess the response to the according treatments. In this work we propose a
method to segment the liver and its lesions from Computed Tomography (CT) scans
using Convolutional Neural Networks (CNNs), that have proven good results in a
variety of computer vision tasks, including medical imaging. The network that
segments the lesions consists of a cascaded architecture, which first focuses
on the region of the liver in order to segment the lesions on it. Moreover, we
train a detector to localize the lesions, and mask the results of the
segmentation network with the positive detections. The segmentation
architecture is based on DRIU, a Fully Convolutional Network (FCN) with side
outputs that work on feature maps of different resolutions, to finally benefit
from the multi-scale information learned by different stages of the network.
The main contribution of this work is the use of a detector to localize the
lesions, which we show to be beneficial to remove false positives triggered by
the segmentation network. Source code and models are available at
https://imatge-upc.github.io/liverseg-2017-nipsws/ .Comment: NIPS 2017 Workshop on Machine Learning for Health (ML4H
Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations
There is no denying how machine learning and computer vision have grown in
the recent years. Their highest advantages lie within their automation,
suitability, and ability to generate astounding results in a matter of seconds
in a reproducible manner. This is aided by the ubiquitous advancements reached
in the computing capabilities of current graphical processing units and the
highly efficient implementation of such techniques. Hence, in this paper, we
survey the key studies that are published between 2014 and 2020, showcasing the
different machine learning algorithms researchers have used to segment the
liver, hepatic-tumors, and hepatic-vasculature structures. We divide the
surveyed studies based on the tissue of interest (hepatic-parenchyma,
hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more
than one task simultaneously. Additionally, the machine learning algorithms are
classified as either supervised or unsupervised, and further partitioned if the
amount of works that fall under a certain scheme is significant. Moreover,
different datasets and challenges found in literature and websites, containing
masks of the aforementioned tissues, are thoroughly discussed, highlighting the
organizers original contributions, and those of other researchers. Also, the
metrics that are used excessively in literature are mentioned in our review
stressing their relevancy to the task at hand. Finally, critical challenges and
future directions are emphasized for innovative researchers to tackle, exposing
gaps that need addressing such as the scarcity of many studies on the vessels
segmentation challenge, and why their absence needs to be dealt with in an
accelerated manner.Comment: 41 pages, 4 figures, 13 equations, 1 table. A review paper on liver
tissues segmentation based on automated ML-based technique
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
Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions
Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio
Hybrid Cascaded Neural Network for Liver Lesion Segmentation
Automatic liver lesion segmentation is a challenging task while having a
significant impact on assisting medical professionals in the designing of
effective treatment and planning proper care. In this paper we propose a
cascaded system that combines both 2D and 3D convolutional neural networks to
effectively segment hepatic lesions. Our 2D network operates on a slice by
slice basis to segment the liver and larger tumors, while we use a 3D network
to detect small lesions that are often missed in a 2D segmentation design. We
employ this algorithm on the LiTS challenge obtaining a Dice score per case of
68.1%, which performs the best among all non pre-trained models and the second
best among published methods. We also perform two-fold cross-validation to
reveal the over- and under-segmentation issues in the LiTS annotations
- …