9,671 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
Recommended from our members
Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures
The quantitative study of cell morphology is of great importance as the structure and condition of cells and their structures can be related to conditions of health or disease. The first step towards that, is the accurate segmentation of cell structures. In this work, we compare five approaches, one traditional and four deep-learning, for the semantic segmentation of the nuclear envelope of cervical cancer cells commonly known as HeLa cells. Images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and four three deep learning architectures: VGG16, ResNet18, Inception-ResNet-v2, and U-Net. Three hundred slices, each 2000 × 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The first three deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The U-Net architecture was trained from scratch with 36, 000 training images and labels of size 128 × 128. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures: VGG16 (93%, 90%), ResNet18 (94%, 88%), Inception-ResNet-v2 (94%, 89%), and U-Net (92%, 56%)
Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
Recent advances in scanning transmission electron and scanning probe
microscopies have opened exciting opportunities in probing the materials
structural parameters and various functional properties in real space with
angstrom-level precision. This progress has been accompanied by an exponential
increase in the size and quality of datasets produced by microscopic and
spectroscopic experimental techniques. These developments necessitate adequate
methods for extracting relevant physical and chemical information from the
large datasets, for which a priori information on the structures of various
atomic configurations and lattice defects is limited or absent. Here we
demonstrate an application of deep neural networks to extract information from
atomically resolved images including location of the atomic species and type of
defects. We develop a 'weakly-supervised' approach that uses information on the
coordinates of all atomic species in the image, extracted via a deep neural
network, to identify a rich variety of defects that are not part of an initial
training set. We further apply our approach to interpret complex atomic and
defect transformation, including switching between different coordination of
silicon dopants in graphene as a function of time, formation of peculiar
silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of
molecular 'rotor'. This deep learning based approach resembles logic of a human
operator, but can be scaled leading to significant shift in the way of
extracting and analyzing information from raw experimental data
Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation
The size of nuclei in histological preparations from excised breast tumors is
predictive of patient outcome (large nuclei indicate poor outcome).
Pathologists take into account nuclear size when performing breast cancer
grading. In addition, the mean nuclear area (MNA) has been shown to have
independent prognostic value. The straightforward approach to measuring nuclear
size is by performing nuclei segmentation. We hypothesize that given an image
of a tumor region with known nuclei locations, the area of the individual
nuclei and region statistics such as the MNA can be reliably computed directly
from the image data by employing a machine learning model, without the
intermediate step of nuclei segmentation. Towards this goal, we train a deep
convolutional neural network model that is applied locally at each nucleus
location, and can reliably measure the area of the individual nuclei and the
MNA. Furthermore, we show how such an approach can be extended to perform
combined nuclei detection and measurement, which is reminiscent of
granulometry.Comment: Conditionally accepted for MICCAI 201
Nucleus segmentation : towards automated solutions
Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution.Peer reviewe
- …