387 research outputs found
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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
Deep learning in medical imaging and radiation therapy
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd
A review of artificial intelligence in prostate cancer detection on imaging
A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care
Deep Semantic Segmentation of Natural and Medical Images: A Review
The semantic image segmentation task consists of classifying each pixel of an
image into an instance, where each instance corresponds to a class. This task
is a part of the concept of scene understanding or better explaining the global
context of an image. In the medical image analysis domain, image segmentation
can be used for image-guided interventions, radiotherapy, or improved
radiological diagnostics. In this review, we categorize the leading deep
learning-based medical and non-medical image segmentation solutions into six
main groups of deep architectural, data synthesis-based, loss function-based,
sequenced models, weakly supervised, and multi-task methods and provide a
comprehensive review of the contributions in each of these groups. Further, for
each group, we analyze each variant of these groups and discuss the limitations
of the current approaches and present potential future research directions for
semantic image segmentation.Comment: 45 pages, 16 figures. Accepted for publication in Springer Artificial
Intelligence Revie
Fast and Accurate Lung Tumor Spotting and Segmentation for Boundary Delineation on CT Slices In A Coarse-To-Fine Framework
Label noise and class imbalance are two of the critical challenges when training image-based deep neural networks, especially in the biomedical image processing domain. Our work focuses on how to address the two challenges effectively and accurately in the task of lesion segmentation from biomedical/medical images. To address the pixel-level label noise problem, we propose an advanced transfer training and learning approach with a detailed DICOM pre-processing method. To address the tumor/non-tumor class imbalance problem, we exploit a self-adaptive fully convolutional neural network with an automated weight distribution mechanism to spot the Radiomics lung tumor regions accurately. Furthermore, an improved conditional random field method is employed to obtain sophisticated lung tumor contour delineation and segmentation. Finally, our approach has been evaluated using several well-known evaluation metrics on the Lung Tumor segmentation dataset used in the 2018 IEEE VIP-CUP Challenge. Experimental results show that our weakly supervised learning algorithm outperforms other deep models and state-of-the-art approache
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