5,033 research outputs found
On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation
Uncertainty estimation methods are expected to improve the understanding and
quality of computer-assisted methods used in medical applications (e.g.,
neurosurgical interventions, radiotherapy planning), where automated medical
image segmentation is crucial. In supervised machine learning, a common
practice to generate ground truth label data is to merge observer annotations.
However, as many medical image tasks show a high inter-observer variability
resulting from factors such as image quality, different levels of user
expertise and domain knowledge, little is known as to how inter-observer
variability and commonly used fusion methods affect the estimation of
uncertainty of automated image segmentation. In this paper we analyze the
effect of common image label fusion techniques on uncertainty estimation, and
propose to learn the uncertainty among observers. The results highlight the
negative effect of fusion methods applied in deep learning, to obtain reliable
estimates of segmentation uncertainty. Additionally, we show that the learned
observers' uncertainty can be combined with current standard Monte Carlo
dropout Bayesian neural networks to characterize uncertainty of model's
parameters.Comment: Appears in Medical Image Computing and Computer Assisted
Interventions (MICCAI), 201
Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy
Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages
Exploring variability in medical imaging
Although recent successes of deep learning and novel machine learning techniques improved the perfor-
mance of classification and (anomaly) detection in computer vision problems, the application of these
methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this
is the amount of variability that is encountered and encapsulated in human anatomy and subsequently
reflected in medical images. This fundamental factor impacts most stages in modern medical imaging
processing pipelines.
Variability of human anatomy makes it virtually impossible to build large datasets for each disease
with labels and annotation for fully supervised machine learning. An efficient way to cope with this is
to try and learn only from normal samples. Such data is much easier to collect. A case study of such
an automatic anomaly detection system based on normative learning is presented in this work. We
present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative
models, which are trained only utilising normal/healthy subjects.
However, despite the significant improvement in automatic abnormality detection systems, clinical
routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis
and localise abnormalities. Integrating human expert knowledge into the medical imaging processing
pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per-
spective of building an automated medical imaging system, it is still an open issue, to what extent
this kind of variability and the resulting uncertainty are introduced during the training of a model
and how it affects the final performance of the task. Consequently, it is very important to explore the
effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as
on the model’s performance in a specific machine learning task. A thorough investigation of this issue
is presented in this work by leveraging automated estimates for machine learning model uncertainty,
inter-observer variability and segmentation task performance in lung CT scan images.
Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging
was attempted. This state-of-the-art survey includes both conventional pattern recognition methods
and deep learning based methods. It is one of the first literature surveys attempted in the specific
research area.Open Acces
p-i-n heterojunctions with BiFeO3 perovskite nanoparticles and p- and n-type oxides: photovoltaic properties.
We formed p-i-n heterojunctions based on a thin film of BiFeO3 nanoparticles. The perovskite acting as an intrinsic semiconductor was sandwiched between a p-type and an n-type oxide semiconductor as hole- and electron-collecting layer, respectively, making the heterojunction act as an all-inorganic oxide p-i-n device. We have characterized the perovskite and carrier collecting materials, such as NiO and MoO3 nanoparticles as p-type materials and ZnO nanoparticles as the n-type material, with scanning tunneling spectroscopy; from the spectrum of the density of states, we could locate the band edges to infer the nature of the active semiconductor materials. The energy level diagram of p-i-n heterojunctions showed that type-II band alignment formed at the p-i and i-n interfaces, favoring carrier separation at both of them. We have compared the photovoltaic properties of the perovskite in p-i-n heterojunctions and also in p-i and i-n junctions. From current-voltage characteristics and impedance spectroscopy, we have observed that two depletion regions were formed at the p-i and i-n interfaces of a p-i-n heterojunction. The two depletion regions operative at p-i-n heterojunctions have yielded better photovoltaic properties as compared to devices having one depletion region in the p-i or the i-n junction. The results evidenced photovoltaic devices based on all-inorganic oxide, nontoxic, and perovskite materials
Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection
The projection of planar MRI data onto a spherical surface is equivalent to a
nonlinear image transformation that retains global anatomical information. By
incorporating this image transformation process in our proposed spherical
projection-based U-Net (SPU-Net) segmentation model design, multiple
independent segmentation predictions can be obtained from a single MRI. The
final segmentation is the average of all available results, and the variation
can be visualized as a pixel-wise uncertainty map. An uncertainty score was
introduced to evaluate and compare the performance of uncertainty measurements.
The proposed SPU-Net model was implemented on the basis of 369 glioma patients
with MP-MRI scans (T1, T1-Ce, T2, and FLAIR). Three SPU-Net models were trained
to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT),
respectively. The SPU-Net model was compared with (1) the classic U-Net model
with test-time augmentation (TTA) and (2) linear scaling-based U-Net (LSU-Net)
segmentation models in terms of both segmentation accuracy (Dice coefficient,
sensitivity, specificity, and accuracy) and segmentation uncertainty
(uncertainty map and uncertainty score). The developed SPU-Net model
successfully achieved low uncertainty for correct segmentation predictions
(e.g., tumor interior or healthy tissue interior) and high uncertainty for
incorrect results (e.g., tumor boundaries). This model could allow the
identification of missed tumor targets or segmentation errors in U-Net.
Quantitatively, the SPU-Net model achieved the highest uncertainty scores for
three segmentation targets (ET/TC/WT): 0.826/0.848/0.936, compared to
0.784/0.643/0.872 using the U-Net with TTA and 0.743/0.702/0.876 with the
LSU-Net (scaling factor = 2). The SPU-Net also achieved statistically
significantly higher Dice coefficients, underscoring the improved segmentation
accuracy.Comment: 31 pages, 9 figures, 1 tabl
A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging.
This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development
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