900 research outputs found
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizing the
semantic and clinical efficacy of automatic segmentation methods relevant for
monitoring osteoarthritis progression.
Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at
two timepoints with ground-truth articular (femoral, tibial, patellar)
cartilage and meniscus segmentations was standardized. Challenge submissions
and a majority-vote ensemble were evaluated using Dice score, average symmetric
surface distance, volumetric overlap error, and coefficient of variation on a
hold-out test set. Similarities in network segmentations were evaluated using
pairwise Dice correlations. Articular cartilage thickness was computed per-scan
and longitudinally. Correlation between thickness error and segmentation
metrics was measured using Pearson's coefficient. Two empirical upper bounds
for ensemble performance were computed using combinations of model outputs that
consolidated true positives and true negatives.
Results: Six teams (T1-T6) submitted entries for the challenge. No
significant differences were observed across all segmentation metrics for all
tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice
correlations between network pairs were high (>0.85). Per-scan thickness errors
were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal
bias (<0.03mm). Low correlations (<0.41) were observed between segmentation
metrics and thickness error. The majority-vote ensemble was comparable to top
performing networks (p=1.0). Empirical upper bound performances were similar
for both combinations (p=1.0).
Conclusion: Diverse networks learned to segment the knee similarly where high
segmentation accuracy did not correlate to cartilage thickness accuracy. Voting
ensembles did not outperform individual networks but may help regularize
individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo
NiftyNet: a deep-learning platform for medical imaging
Medical image analysis and computer-assisted intervention problems are
increasingly being addressed with deep-learning-based solutions. Established
deep-learning platforms are flexible but do not provide specific functionality
for medical image analysis and adapting them for this application requires
substantial implementation effort. Thus, there has been substantial duplication
of effort and incompatible infrastructure developed across many research
groups. This work presents the open-source NiftyNet platform for deep learning
in medical imaging. The ambition of NiftyNet is to accelerate and simplify the
development of these solutions, and to provide a common mechanism for
disseminating research outputs for the community to use, adapt and build upon.
NiftyNet provides a modular deep-learning pipeline for a range of medical
imaging applications including segmentation, regression, image generation and
representation learning applications. Components of the NiftyNet pipeline
including data loading, data augmentation, network architectures, loss
functions and evaluation metrics are tailored to, and take advantage of, the
idiosyncracies of medical image analysis and computer-assisted intervention.
NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D
and 3D images and computational graphs by default.
We present 3 illustrative medical image analysis applications built using
NiftyNet: (1) segmentation of multiple abdominal organs from computed
tomography; (2) image regression to predict computed tomography attenuation
maps from brain magnetic resonance images; and (3) generation of simulated
ultrasound images for specified anatomical poses.
NiftyNet enables researchers to rapidly develop and distribute deep learning
solutions for segmentation, regression, image generation and representation
learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge
Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6
figures; Update includes additional applications, updated author list and
formatting for journal submissio
Healthy kidney segmentation in the dce-mr images using a convolutional neural network and temporal signal characteristics
Quantification of renal perfusion based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) requires determination of signal intensity time courses in the region of renal parenchyma. Thus, selection of voxels representing the kidney must be accomplished with special care and constitutes one of the major technical limitations which hampers wider usage of this technique as a standard clinical routine. Manual segmentation of renal compartments—even if performed by experts—is a common source of decreased repeatability and reproducibility. In this paper, we present a processing framework for the automatic kidney segmentation in DCE-MR images. The framework consists of two stages. Firstly, kidney masks are generated using a convolutional neural network. Then, mask voxels are classified to one of three regions—cortex, medulla, and pelvis–based on DCE-MRI signal intensity time courses. The proposed approach was evaluated on a cohort of 10 healthy volunteers who underwent the DCE-MRI examination. MRI scanning was repeated on two time events within a 10-day interval. For semantic segmentation task we employed a classic U-Net architecture, whereas experiments on voxel classification were performed using three alternative algorithms—support vector machines, logistic regression and extreme gradient boosting trees, among which SVM produced the most accurate results. Both segmentation and classification steps were accomplished by a series of models, each trained separately for a given subject using the data from other participants only. The mean achieved accuracy of the whole kidney segmentation was 94% in terms of IoU coefficient. Cortex, medulla and pelvis were segmented with IoU ranging from 90 to 93% depending on the tissue and body side. The results were also validated by comparing image-derived perfusion parameters with ground truth measurements of glomerular filtration rate (GFR). The repeatability of GFR calculation, as assessed by the coefficient of variation was determined at the level of 14.5 and 17.5% for the left and right kidney, respectively and it improved relative to manual segmentation. Reproduciblity, in turn, was evaluated by measuring agreement between image-derived and iohexol-based GFR values. The estimated absolute mean differences were equal to 9.4 and 12.9 mL/min/1.73 m2 for scanning sessions 1 and 2 and the proposed automated segmentation method. The result for session 2 was comparable with manual segmentation, whereas for session 1 reproducibility in the automatic pipeline was weaker.publishedVersio
Evaluation with an Independent Dataset of a Deep Learning-based Left Atrium Segmentation Method
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Director/s: Gaspar Delso i Roser Sala. Tutor: Manel PuigAtrial fibrillation (AF) is the most prevalent type of arrhythmia nowadays. Even though it is
associated with significant morbidity and mortality, there is still a substantial lack of basic
understanding of the left atrium (LA) and pulmonary veins (PVs) anatomical structure that curbs
the performance of current clinical treatments for the disease. Thus, segmentation and 3D
reconstruction of the LA and PVs are of crucial importance for the diagnosis and treatment of AF.
In this context, cardiac 3D Late Gadolinium Magnetic Resonance Imaging (LGE-MRI) appear as a
very good tool for cardiac tissue characterization and myocardial fibrosis detection. In fact, these
images have been proofed as reliable predictors of catheter ablation success, which is often the
chosen treatment for AF patients.
Several manual and semi-automatic segmentation tools from LGE-MRI scans are currently in use,
but these are very time-consuming and highly prone to errors, hence the need for an automatic
segmentation approach.
With the rise of deep learning and convolutional neural networks, a number of automatic schemes
are being developed. In this project, we evaluate a model that has been developed at the Hospital
ClĂnic de Barcelona for obtaining an automatic segmentation of the LA using a deep learning
architecture. Concretely, we tested this model with an independent set of images from another MRI
vendor, and we obtained a set of quantitative and qualitative measures to validate the results.
For the pursuit of our aims, this work begins with the state-of-the-art for LA segmentation of LGEMRI
scans and with a market analysis of the field. We then present our proposed solution together
with the obtained results and the corresponding conclusions
- …