54 research outputs found
End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction
We present a multi-stage 3D computer-aided detection and diagnosis (CAD)
model for automated localization of clinically significant prostate cancer
(csPCa) in bi-parametric MR imaging (bpMRI). Deep attention mechanisms drive
its detection network, targeting salient structures and highly discriminative
feature dimensions across multiple resolutions. Its goal is to accurately
identify csPCa lesions from indolent cancer and the wide range of benign
pathology that can afflict the prostate gland. Simultaneously, a decoupled
residual classifier is used to achieve consistent false positive reduction,
without sacrificing high sensitivity or computational efficiency. In order to
guide model generalization with domain-specific clinical knowledge, a
probabilistic anatomical prior is used to encode the spatial prevalence and
zonal distinction of csPCa. Using a large dataset of 1950 prostate bpMRI paired
with radiologically-estimated annotations, we hypothesize that such CNN-based
models can be trained to detect biopsy-confirmed malignancies in an independent
cohort.
For 486 institutional testing scans, the 3D CAD system achieves
83.695.22% and 93.192.96% detection sensitivity at 0.50 and 1.46
false positive(s) per patient, respectively, with 0.8820.030 AUROC in
patient-based diagnosis significantly outperforming four state-of-the-art
baseline architectures (U-SEResNet, UNet++, nnU-Net, Attention U-Net) from
recent literature. For 296 external biopsy-confirmed testing scans, the
ensembled CAD system shares moderate agreement with a consensus of expert
radiologists (76.69%; 0.510.04) and independent pathologists
(81.08%; 0.560.06); demonstrating strong generalization to
histologically-confirmed csPCa diagnosis.Comment: Accepted to MedIA: Medical Image Analysis. This manuscript
incorporates and expands upon our 2020 Medical Imaging Meets NeurIPS Workshop
paper (arXiv:2011.00263
Supervised Uncertainty Quantification for Segmentation with Multiple Annotations
The accurate estimation of predictive uncertainty carries importance in
medical scenarios such as lung node segmentation. Unfortunately, most existing
works on predictive uncertainty do not return calibrated uncertainty estimates,
which could be used in practice. In this work we exploit multi-grader
annotation variability as a source of 'groundtruth' aleatoric uncertainty,
which can be treated as a target in a supervised learning problem. We combine
this groundtruth uncertainty with a Probabilistic U-Net and test on the
LIDC-IDRI lung nodule CT dataset and MICCAI2012 prostate MRI dataset. We find
that we are able to improve predictive uncertainty estimates. We also find that
we can improve sample accuracy and sample diversity. In real-world
applications, our method could inform doctors about the confidence of the
segmentation results.Comment: MICCAI 2019. Fixed a few typo
Complexities of deep learning-based undersampled MR image reconstruction
Artificial intelligence has opened a new path of innovation in magnetic resonance (MR) image reconstruction of undersampled k-space acquisitions. This review offers readers an analysis of the current deep learning-based MR image reconstruction methods. The literature in this field shows exponential growth, both in volume and complexity, as the capabilities of machine learning in solving inverse problems such as image reconstruction are explored. We review the latest developments, aiming to assist researchers and radiologists who are developing new methods or seeking to provide valuable feedback. We shed light on key concepts by exploring the technical intricacies of MR image reconstruction, highlighting the importance of raw datasets and the difficulty of evaluating diagnostic value using standard metrics.Relevance statementIncreasingly complex algorithms output reconstructed images that are difficult to assess for robustness and diagnostic quality, necessitating high-quality datasets and collaboration with radiologists.Key points• Deep learning-based image reconstruction algorithms are increasing both in complexity and performance.• The evaluation of reconstructed images may mistake perceived image quality for diagnostic value.• Collaboration with radiologists is crucial for advancing deep learning technology.</p
Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI
Deep learning-based diagnostic performance increases with more annotated
data, but large-scale manual annotations are expensive and labour-intensive.
Experts evaluate diagnostic images during clinical routine, and write their
findings in reports. Leveraging unlabelled exams paired with clinical reports
could overcome the manual labelling bottleneck. We hypothesise that detection
models can be trained semi-supervised with automatic annotations generated
using model predictions, guided by sparse information from clinical reports. To
demonstrate efficacy, we train clinically significant prostate cancer (csPCa)
segmentation models, where automatic annotations are guided by the number of
clinically significant findings in the radiology reports. We included 7,756
prostate MRI examinations, of which 3,050 were manually annotated. We evaluated
prostate cancer detection performance on 300 exams from an external centre with
histopathology-confirmed ground truth. Semi-supervised training improved
patient-based diagnostic area under the receiver operating characteristic curve
from to () and improved
lesion-based sensitivity at one false positive per case from
to (). Semi-supervised training was 14 more
annotation-efficient for case-based performance and 6 more
annotation-efficient for lesion-based performance. This improved performance
demonstrates the feasibility of our training procedure. Source code is publicly
available at github.com/DIAGNijmegen/Report-Guided-Annotation. Best csPCa
detection algorithm is available at
grand-challenge.org/algorithms/bpmri-cspca-detection-report-guided-annotations/
Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning
The ability to adapt medical image segmentation networks for a novel class
such as an unseen anatomical or pathological structure, when only a few
labelled examples of this class are available from local healthcare providers,
is sought-after. This potentially addresses two widely recognised limitations
in deploying modern deep learning models to clinical practice,
expertise-and-labour-intensive labelling and cross-institution generalisation.
This work presents the first 3D few-shot interclass segmentation network for
medical images, using a labelled multi-institution dataset from prostate cancer
patients with eight regions of interest. We propose an image alignment module
registering the predicted segmentation of both query and support data, in a
standard prototypical learning algorithm, to a reference atlas space. The
built-in registration mechanism can effectively utilise the prior knowledge of
consistent anatomy between subjects, regardless whether they are from the same
institution or not. Experimental results demonstrated that the proposed
registration-assisted prototypical learning significantly improved segmentation
accuracy (p-values<0.01) on query data from a holdout institution, with varying
availability of support data from multiple institutions. We also report the
additional benefits of the proposed 3D networks with 75% fewer parameters and
an arguably simpler implementation, compared with existing 2D few-shot
approaches that segment 2D slices of volumetric medical images
Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer
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