2,025 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
Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length
Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the annotation effort required to achieve a certain level of accuracy when labelling such a stack. The framework suggests, at every iteration, a specific region of interest (ROI) in one of the images for manual delineation. Using a deep segmentation neural network and a mixed cross-entropy loss function, we propose a principled strategy to estimate class probabilities for the whole stack, conditioned on heterogeneous partial segmentations of the 2D images, as well as on weak supervision in the form of image indices that bound each ROI. Using the estimated probabilities, we propose a novel active learning criterion based on predictions for the estimated segmentation performance and delineation effort, measured with average Dice scores and total delineated boundary length, respectively, rather than common surrogates such as entropy. The query strategy suggests the ROI that is expected to maximise the ratio between performance and effort, while considering the adjacency of structures that may have already been labelled – which decrease the length of the boundary to trace. We provide quantitative results on synthetically deformed MRI scans and real histological data, showing that our framework can reduce labelling effort by up to 60–70% without compromising accuracy
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation
As deep learning methods continue to improve medical image segmentation
performance, data annotation is still a big bottleneck due to the
labor-intensive and time-consuming burden on medical experts, especially for 3D
images. To significantly reduce annotation efforts while attaining competitive
segmentation accuracy, we propose a self-learning and one-shot learning based
framework for 3D medical image segmentation by annotating only one slice of
each 3D image. Our approach takes two steps: (1) self-learning of a
reconstruction network to learn semantic correspondence among 2D slices within
3D images, and (2) representative selection of single slices for one-shot
manual annotation and propagating the annotated data with the well-trained
reconstruction network. Extensive experiments verify that our new framework
achieves comparable performance with less than 1% annotated data compared with
fully supervised methods and generalizes well on several out-of-distribution
testing sets
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation
Accurate tooth volume segmentation is a prerequisite for computer-aided
dental analysis. Deep learning-based tooth segmentation methods have achieved
satisfying performances but require a large quantity of tooth data with ground
truth. The dental data publicly available is limited meaning the existing
methods can not be reproduced, evaluated and applied in clinical practice. In
this paper, we establish a 3D dental CBCT dataset CTooth+, with 22 fully
annotated volumes and 146 unlabeled volumes. We further evaluate several
state-of-the-art tooth volume segmentation strategies based on fully-supervised
learning, semi-supervised learning and active learning, and define the
performance principles. This work provides a new benchmark for the tooth volume
segmentation task, and the experiment can serve as the baseline for future
AI-based dental imaging research and clinical application development
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