20 research outputs found
A deep level set method for image segmentation
This paper proposes a novel image segmentation approachthat integrates fully
convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the
integrated method can incorporatesmoothing and prior information to achieve an
accurate segmentation.Furthermore, different than using the level set model as
a post-processingtool, we integrate it into the training phase to fine-tune the
FCN. Thisallows the use of unlabeled data during training in a
semi-supervisedsetting. Using two types of medical imaging data (liver CT and
left ven-tricle MRI data), we show that the integrated method achieves
goodperformance even when little training data is available, outperformingthe
FCN or the level set model alone
Unbiased Shape Compactness for Segmentation
We propose to constrain segmentation functionals with a dimensionless,
unbiased and position-independent shape compactness prior, which we solve
efficiently with an alternating direction method of multipliers (ADMM).
Involving a squared sum of pairwise potentials, our prior results in a
challenging high-order optimization problem, which involves dense (fully
connected) graphs. We split the problem into a sequence of easier sub-problems,
each performed efficiently at each iteration: (i) a sparse-matrix inversion
based on Woodbury identity, (ii) a closed-form solution of a cubic equation and
(iii) a graph-cut update of a sub-modular pairwise sub-problem with a sparse
graph. We deploy our prior in an energy minimization, in conjunction with a
supervised classifier term based on CNNs and standard regularization
constraints. We demonstrate the usefulness of our energy in several medical
applications. In particular, we report comprehensive evaluations of our fully
automated algorithm over 40 subjects, showing a competitive performance for the
challenging task of abdominal aorta segmentation in MRI.Comment: Accepted at MICCAI 201
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
Convolutional neural networks have been successfully applied to semantic
segmentation problems. However, there are many problems that are inherently not
pixel-wise classification problems but are nevertheless frequently formulated
as semantic segmentation. This ill-posed formulation consequently necessitates
hand-crafted scenario-specific and computationally expensive post-processing
methods to convert the per pixel probability maps to final desired outputs.
Generative adversarial networks (GANs) can be used to make the semantic
segmentation network output to be more realistic or better
structure-preserving, decreasing the dependency on potentially complex
post-processing. In this work, we propose EL-GAN: a GAN framework to mitigate
the discussed problem using an embedding loss. With EL-GAN, we discriminate
based on learned embeddings of both the labels and the prediction at the same
time. This results in more stable training due to having better discriminative
information, benefiting from seeing both `fake' and `real' predictions at the
same time. This substantially stabilizes the adversarial training process. We
use the TuSimple lane marking challenge to demonstrate that with our proposed
framework it is viable to overcome the inherent anomalies of posing it as a
semantic segmentation problem. Not only is the output considerably more similar
to the labels when compared to conventional methods, the subsequent
post-processing is also simpler and crosses the competitive 96% accuracy
threshold.Comment: 14 pages, 7 figure
A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI
With respect to spatial overlap, CNN-based segmentation of short axis
cardiovascular magnetic resonance (CMR) images has achieved a level of
performance consistent with inter observer variation. However, conventional
training procedures frequently depend on pixel-wise loss functions, limiting
optimisation with respect to extended or global features. As a result, inferred
segmentations can lack spatial coherence, including spurious connected
components or holes. Such results are implausible, violating the anticipated
topology of image segments, which is frequently known a priori. Addressing this
challenge, published work has employed persistent homology, constructing
topological loss functions for the evaluation of image segments against an
explicit prior. Building a richer description of segmentation topology by
considering all possible labels and label pairs, we extend these losses to the
task of multi-class segmentation. These topological priors allow us to resolve
all topological errors in a subset of 150 examples from the ACDC short axis CMR
training data set, without sacrificing overlap performance.Comment: To be presented at the STACOM workshop at MICCAI 202
Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance
© 2020, Springer Nature Switzerland AG. Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural information of tissues. We propose a self-supervised approach to incorporate semantic guidance into a GAN based stain normalization framework and preserve detailed structural information. Our method does not require manual segmentation maps which is a significant advantage over existing methods. We integrate semantic information at different layers between a pre-trained semantic network and the stain color normalization network. The proposed scheme outperforms other color normalization methods leading to better classification and segmentation performance
Development and Validation of a Deep Learning-Based Microsatellite Instability Predictor from Prostate Cancer Whole-Slide Images
Microsatellite instability-high (MSI-H) is a tumor agnostic biomarker for
immune checkpoint inhibitor therapy. However, MSI status is not routinely
tested in prostate cancer, in part due to low prevalence and assay cost. As
such, prediction of MSI status from hematoxylin and eosin (H&E) stained
whole-slide images (WSIs) could identify prostate cancer patients most likely
to benefit from confirmatory testing and becoming eligible for immunotherapy.
Prostate biopsies and surgical resections from de-identified records of
consecutive prostate cancer patients referred to our institution were analyzed.
Their MSI status was determined by next generation sequencing. Patients before
a cutoff date were split into an algorithm development set (n=4015, MSI-H 1.8%)
and a paired validation set (n=173, MSI-H 19.7%) that consisted of two serial
sections from each sample, one stained and scanned internally and the other at
an external site. Patients after the cutoff date formed the temporal validation
set (n=1350, MSI-H 2.3%). Attention-based multiple instance learning models
were trained to predict MSI-H from H&E WSIs. The MSI-H predictor achieved area
under the receiver operating characteristic curve values of 0.78 (95% CI
[0.69-0.86]), 0.72 (95% CI [0.63-0.81]), and 0.72 (95% CI [0.62-0.82]) on the
internally prepared, externally prepared, and temporal validation sets,
respectively. While MSI-H status is significantly correlated with Gleason
score, the model remained predictive within each Gleason score subgroup. In
summary, we developed and validated an AI-based MSI-H diagnostic model on a
large real-world cohort of routine H&E slides, which effectively generalized to
externally stained and scanned samples and a temporally independent validation
cohort. This algorithm has the potential to direct prostate cancer patients
toward immunotherapy and to identify MSI-H cases secondary to Lynch syndrome
Gender classification from 3D face images using multi-task sparse representation over reduced
International audienceIn this paper, we address the problem of gender classification based on facial images. The Speeded Up Robust Feature (SURF) algorithm descriptors are used as features to built dictionaries and a multi-task Sparse Representation Classification (SRC) is used as classifier to determine the gender of an individual face. Our approach uses smaller and compact dictionaries by removing the redundant atoms from the constructed ones. The feasibility of using the SURF on the shape index map for gender classification is demonstrated through experimental investigation conducted on FRGCv2 dataset. The proposed approach achieves 91.04±1.19% of correct gender classification rate using only 5% of the size of the dictionary and 97.83 ± 0.76% is obtained using 23% of the size of the dictionary
Face Recognition from Unconstrained 3D Face Images using Multi-Task Sparse Representation
International audienceWe propose and evaluate a three-dimensional (3D) face recognition approach that applies the speeded up robust feature (SURF) algorithm to the depth representation of shape index map, under real-world conditions, using only a single gallery sample for each subject. First, the 3D scans are preprocessed, then SURF is applied on the shape index map to find interest points and their descriptors. Each 3D face scan is represented by keypoints descriptors, and a large dictionary is built from all the gallery descriptors. At the recognition step, descriptors of a probe face scan are sparsely represented by the dictionary. A multitask sparse representation classification is used to determine the identity of each probe face. The feasibility of the approach that uses the SURF algorithm on the shape index map for face identification/authentication is checked through an experimental investigation conducted on Bosphorus, University of Milano Bicocca, and CASIA 3D datasets. It achieves an overall rank one recognition rate of 97.75%, 80.85%, and 95.12%, respectively, on these datasets