687 research outputs found
Semi-supervised learning towards automated segmentation of PET images with limited annotations: Application to lymphoma patients
The time-consuming task of manual segmentation challenges routine systematic
quantification of disease burden. Convolutional neural networks (CNNs) hold
significant promise to reliably identify locations and boundaries of tumors
from PET scans. We aimed to leverage the need for annotated data via
semi-supervised approaches, with application to PET images of diffuse large
B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL).
We analyzed 18F-FDG PET images of 292 patients with PMBCL (n=104) and DLBCL
(n=188) (n=232 for training and validation, and n=60 for external testing). We
employed FCM and MS losses for training a 3D U-Net with different levels of
supervision: i) fully supervised methods with labeled FCM (LFCM) as well as
Unified focal and Dice loss functions, ii) unsupervised methods with Robust FCM
(RFCM) and Mumford-Shah (MS) loss functions, and iii) Semi-supervised methods
based on FCM (RFCM+LFCM), as well as MS loss in combination with supervised
Dice loss (MS+Dice). Unified loss function yielded higher Dice score (mean +/-
standard deviation (SD)) (0.73 +/- 0.03; 95% CI, 0.67-0.8) compared to Dice
loss (p-value<0.01). Semi-supervised (RFCM+alpha*LFCM) with alpha=0.3 showed
the best performance, with a Dice score of 0.69 +/- 0.03 (95% CI, 0.45-0.77)
outperforming (MS+alpha*Dice) for any supervision level (any alpha) (p<0.01).
The best performer among (MS+alpha*Dice) semi-supervised approaches with
alpha=0.2 showed a Dice score of 0.60 +/- 0.08 (95% CI, 0.44-0.76) compared to
another supervision level in this semi-supervised approach (p<0.01).
Semi-supervised learning via FCM loss (RFCM+alpha*LFCM) showed improved
performance compared to supervised approaches. Considering the time-consuming
nature of expert manual delineations and intra-observer variabilities,
semi-supervised approaches have significant potential for automated
segmentation workflows
Variational and deep learning segmentation of very-low-contrast X-ray computed tomography images of carbon/epoxy woven composites
The purpose of this work is to find an effective image segmentation method for lab-based micro-tomography (mu-CT) data of carbon fiber reinforced polymers (CFRP) with insufficient contrast-to-noise ratio. The segmentation is the first step in creating a realistic geometry (based on mu-CT) for finite element modelling of textile composites on meso-scale. Noise in X-ray imaging data of carbon/polymer composites forms a challenge for this segmentation due to the very low X-ray contrast between fiber and polymer and unclear fiber gradients. To the best of our knowledge, segmentation of mu-CT images of carbon/polymer textile composites with low resolution data (voxel size close to the fiber diameter) remains poorly documented. In this paper, we propose and evaluate different approaches for solving the segmentation problem: variational on the one hand and deep-learning-based on the other. In the author's view, both strategies present a novel and reliable ground for the segmentation of mu-CT data of CFRP woven composites. The predictions of both approaches were evaluated against a manual segmentation of the volume, constituting our "ground truth", which provides quantitative data on the segmentation accuracy. The highest segmentation accuracy (about 4.7% in terms of voxel-wise Dice similarity) was achieved using the deep learning approach with U-Net neural network
Box-supervised Instance Segmentation with Level Set Evolution
In contrast to the fully supervised methods using pixel-wise mask labels,
box-supervised instance segmentation takes advantage of the simple box
annotations, which has recently attracted a lot of research attentions. In this
paper, we propose a novel single-shot box-supervised instance segmentation
approach, which integrates the classical level set model with deep neural
network delicately. Specifically, our proposed method iteratively learns a
series of level sets through a continuous Chan-Vese energy-based function in an
end-to-end fashion. A simple mask supervised SOLOv2 model is adapted to predict
the instance-aware mask map as the level set for each instance. Both the input
image and its deep features are employed as the input data to evolve the level
set curves, where a box projection function is employed to obtain the initial
boundary. By minimizing the fully differentiable energy function, the level set
for each instance is iteratively optimized within its corresponding bounding
box annotation. The experimental results on four challenging benchmarks
demonstrate the leading performance of our proposed approach to robust instance
segmentation in various scenarios. The code is available at:
https://github.com/LiWentomng/boxlevelset.Comment: 17 page, 4figures, ECCV202
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed
inverse problem. A key aspect is to formalize the steps of reconstruction and
task as appropriate estimators (non-randomized decision rules) in statistical
estimation problems. The implementation makes use of (deep) neural networks to
provide a differentiable parametrization of the family of estimators for both
steps. These networks are combined and jointly trained against suitable
supervised training data in order to minimize a joint differentiable loss
function, resulting in an end-to-end task adapted reconstruction method. The
suggested framework is generic, yet adaptable, with a plug-and-play structure
for adjusting both the inverse problem and the task at hand. More precisely,
the data model (forward operator and statistical model of the noise) associated
with the inverse problem is exchangeable, e.g., by using neural network
architecture given by a learned iterative method. Furthermore, any task that is
encodable as a trainable neural network can be used. The approach is
demonstrated on joint tomographic image reconstruction, classification and
joint tomographic image reconstruction segmentation
Single-Image based unsupervised joint segmentation and denoising
In this work, we develop an unsupervised method for the joint segmentation
and denoising of a single image. To this end, we combine the advantages of a
variational segmentation method with the power of a self-supervised,
single-image based deep learning approach. One major strength of our method
lies in the fact, that in contrast to data-driven methods, where huge amounts
of labeled samples are necessary, our model can segment an image into multiple
meaningful regions without any training database. Further, we introduce a novel
energy functional in which denoising and segmentation are coupled in a way that
both tasks benefit from each other. The limitations of existing single-image
based variational segmentation methods, which are not capable of dealing with
high noise or generic texture, are tackled by this specific combination with
self-supervised image denoising. We propose a unified optimisation strategy and
show that, especially for very noisy images available in microscopy, our
proposed joint approach outperforms its sequential counterpart as well as
alternative methods focused purely on denoising or segmentation. Another
comparison is conducted with a supervised deep learning approach designed for
the same application, highlighting the good performance of our approach
A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm
Euler's Elastica based unsupervised segmentation models have strong
capability of completing the missing boundaries for existing objects in a clean
image, but they are not working well for noisy images. This paper aims to
establish a Euler's Elastica based approach that properly deals with random
noises to improve the segmentation performance for noisy images. We solve the
corresponding optimization problem via using the progressive hedging algorithm
(PHA) with a step length suggested by the alternating direction method of
multipliers (ADMM). Technically, all the simplified convex versions of the
subproblems derived from the major framework of PHA can be obtained by using
the curvature weighted approach and the convex relaxation method. Then an
alternating optimization strategy is applied with the merits of using some
powerful accelerating techniques including the fast Fourier transform (FFT) and
generalized soft threshold formulas. Extensive experiments have been conducted
on both synthetic and real images, which validated some significant gains of
the proposed segmentation models and demonstrated the advantages of the
developed algorithm
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