81 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
Intelligent Cervical Spine Fracture Detection Using Deep Learning Methods
Cervical spine fractures constitute a critical medical emergency, with the
potential for lifelong paralysis or even fatality if left untreated or
undetected. Over time, these fractures can deteriorate without intervention. To
address the lack of research on the practical application of deep learning
techniques for the detection of spine fractures, this study leverages a dataset
containing both cervical spine fractures and non-fractured computed tomography
images. This paper introduces a two-stage pipeline designed to identify the
presence of cervical vertebrae in each image slice and pinpoint the location of
fractures. In the first stage, a multi-input network, incorporating image and
image metadata, is trained. This network is based on the Global Context Vision
Transformer, and its performance is benchmarked against popular deep learning
image classification model. In the second stage, a YOLOv8 model is trained to
detect fractures within the images, and its effectiveness is compared to
YOLOv5. The obtained results indicate that the proposed algorithm significantly
reduces the workload of radiologists and enhances the accuracy of fracture
detection
Unsupervised domain adaptation for vertebrae detection and identification in 3D CT volumes using a domain sanity loss
A variety of medical computer vision applications analyze 2D slices of computed tomography (CT) scans, whereas axial slices from the body trunk region are usually identified based on their relative position to the spine. A limitation of such systems is that either the correct slices must be extracted manually or labels of the vertebrae are required for each CT scan to develop an automated extraction system. In this paper, we propose an unsupervised domain adaptation (UDA) approach for vertebrae detection and identification based on a novel Domain Sanity Loss (DSL) function. With UDA the model’s knowledge learned on a publicly available (source) data set can be transferred to the target domain without using target labels, where the target domain is defined by the specific setup (CT modality, study protocols, applied pre- and processing) at the point of use (e.g., a specific clinic with its specific CT study protocols). With our approach, a model is trained on the source and target data set in parallel. The model optimizes a supervised loss for labeled samples from the source domain and the DSL loss function based on domain-specific “sanity checks” for samples from the unlabeled target domain. Without using labels from the target domain, we are able to identify vertebra centroids with an accuracy of 72.8%. By adding only ten target labels during training the accuracy increases to 89.2%, which is on par with the current state-of-the-art for full supervised learning, while using about 20 times less labels. Thus, our model can be used to extract 2D slices from 3D CT scans on arbitrary data sets fully automatically without requiring an extensive labeling effort, contributing to the clinical adoption of medical imaging by hospitals
- …