1,065 research outputs found
Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application
In this contribution, we used the GrowCut segmentation algorithm publicly
available in three-dimensional Slicer for three-dimensional segmentation of
vertebral bodies. To the best of our knowledge, this is the first time that the
GrowCut method has been studied for the usage of vertebral body segmentation.
In brief, we found that the GrowCut segmentation times were consistently less
than the manual segmentation times. Hence, GrowCut provides an alternative to a
manual slice-by-slice segmentation process.Comment: 10 page
Fully Automatic Segmentation of Lumbar Vertebrae from CT Images using Cascaded 3D Fully Convolutional Networks
We present a method to address the challenging problem of segmentation of
lumbar vertebrae from CT images acquired with varying fields of view. Our
method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting
of a localization FCN and a segmentation FCN. More specifically, in the first
step we train a regression 3D FCN (we call it "LocalizationNet") to find the
bounding box of the lumbar region. After that, a 3D U-net like FCN (we call it
"SegmentationNet") is then developed, which after training, can perform a
pixel-wise multi-class segmentation to map a cropped lumber region volumetric
data to its volume-wise labels. Evaluated on publicly available datasets, our
method achieved an average Dice coefficient of 95.77 0.81% and an average
symmetric surface distance of 0.37 0.06 mm.Comment: 5 pages and 5 figure
Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences
In this article, we present a graph-based method using a cubic template for
volumetric segmentation of vertebrae in magnetic resonance imaging (MRI)
acquisitions. The user can define the degree of deviation from a regular cube
via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph
with two terminal nodes (s-t-network), where the nodes of the graph correspond
to a cubic-shaped subset of the image's voxels. The weightings of the graph's
terminal edges, which connect every node with a virtual source s or a virtual
sink t, represent the affinity of a voxel to the vertebra (source) and to the
background (sink). Furthermore, a set of infinite weighted and non-terminal
edges implements the smoothness term. After graph construction, a minimal
s-t-cut is calculated within polynomial computation time, which splits the
nodes into two disjoint units. Subsequently, the segmentation result is
determined out of the source-set. A quantitative evaluation of a C++
implementation of the algorithm resulted in an average Dice Similarity
Coefficient (DSC) of 81.33% and a running time of less than a minute.Comment: 23 figures, 2 tables, 43 references, PLoS ONE 9(4): e9338
Automated detection, labelling and radiological grading of clinical spinal MRIs
Spinal magnetic resonance (MR) scans are a vital tool for diagnosing the cause of back pain for many diseases and conditions. However, interpreting clinically useful information from these scans can be challenging, time-consuming and hard to reproduce across different radiologists. In this paper, we alleviate these problems by introducing a multi-stage automated pipeline for analysing spinal MR scans. This pipeline first detects and labels vertebral bodies across several commonly used sequences (e.g. T1w, T2w and STIR) and fields of view (e.g. lumbar, cervical, whole spine). Using these detections it then performs automated diagnosis for several spinal disorders, including intervertebral disc degenerative changes in T1w and T2w lumbar scans, and spinal metastases, cord compression and vertebral fractures. To achieve this, we propose a new method of vertebrae detection and labelling, using vector fields to group together detected vertebral landmarks and a language-modelling inspired beam search to determine the corresponding levels of the detections. We also employ a new transformer-based architecture to perform radiological grading which incorporates context from multiple vertebrae and sequences, as a real radiologist would. The performance of each stage of the pipeline is tested in isolation on several clinical datasets, each consisting of 66 to 421 scans. The outputs are compared to manual annotations of expert radiologists, demonstrating accurate vertebrae detection across a range of scan parameters. Similarly, the model’s grading predictions for various types of disc degeneration and detection of spinal metastases closely match those of an expert radiologist. To aid future research, our code and trained models are made publicly available
A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI
We propose a novel convolutional method for the detection and identification
of vertebrae in whole spine MRIs. This involves using a learnt vector field to
group detected vertebrae corners together into individual vertebral bodies and
convolutional image-to-image translation followed by beam search to label
vertebral levels in a self-consistent manner. The method can be applied without
modification to lumbar, cervical and thoracic-only scans across a range of
different MR sequences. The resulting system achieves 98.1% detection rate and
96.5% identification rate on a challenging clinical dataset of whole spine
scans and matches or exceeds the performance of previous systems on lumbar-only
scans. Finally, we demonstrate the clinical applicability of this method, using
it for automated scoliosis detection in both lumbar and whole spine MR scans.Comment: Accepted full paper to Medical Image Computing and Computer Assisted
Intervention 2020. 11 pages plus appendi
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
AI MSK clinical applications: spine imaging
Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions
Lumbar spine segmentation in MR images: a dataset and a public benchmark
This paper presents a large publicly available multi-center lumbar spine
magnetic resonance imaging (MRI) dataset with reference segmentations of
vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes
447 sagittal T1 and T2 MRI series from 218 patients with a history of low back
pain. It was collected from four different hospitals and was divided into a
training (179 patients) and validation (39 patients) set. An iterative data
annotation approach was used by training a segmentation algorithm on a small
part of the dataset, enabling semi-automatic segmentation of the remaining
images. The algorithm provided an initial segmentation, which was subsequently
reviewed, manually corrected, and added to the training data. We provide
reference performance values for this baseline algorithm and nnU-Net, which
performed comparably. We set up a continuous segmentation challenge to allow
for a fair comparison of different segmentation algorithms. This study may
encourage wider collaboration in the field of spine segmentation, and improve
the diagnostic value of lumbar spine MRI
Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression
[EN] Purpose: The development of automatic and reliable algorithms for the detection and segmentation
of the vertebrae are of great importance prior to any diagnostic task. However, an important problem
found to accurately segment the vertebrae is the presence of the ribs in the thoracic region. To overcome
this problem, a probabilistic atlas of the spine has been developed dealing with the proximity
of other structures, with a special focus on ribs suppression.
Methods: The data sets used consist of Computed Tomography images corresponding to 21 patients
suffering from spinal metastases. Two methods have been combined to obtain the final result: firstly,
an initial segmentation is performed using a fully automatic level-set method; secondly, to refine the
initial segmentation, a 3D volume indicating the probability of each voxel of belonging to the spine
has been developed. In this way, a probability map is generated and deformed to be adapted to each
testing case.
Results: To validate the improvement obtained after applying the atlas, the Dice coefficient (DSC),
the Hausdorff distance (HD), and the mean surface-to-surface distance (MSD) were used. The results
showed up an average of 10 mm of improvement accuracy in terms of HD, obtaining an overall final
average of 15.51 2.74 mm. Also, a global value of 91.01 3.18% in terms of DSC and a MSD of
0.66 0.25 mm were obtained. The major improvement using the atlas was achieved in the thoracic
region, as ribs were almost perfectly suppressed.
Conclusion: The study demonstrated that the atlas is able to detect and appropriately eliminate the
ribs while improving the segmentation accuracy.The authors thank the financial support of the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under Grants TEC2012-33778 and BFU2015-64380-C2-2-R (D.M.) and DPI2013-4572-R (J.D., E.D.)Ruiz-España, S.; Domingo, J.; DĂaz-Parra, A.; Dura, E.; D'ocon-Alcaniz, V.; Arana, E.; Moratal, D. (2017). Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression. Medical Physics. 44(9):4695-4707. https://doi.org/10.1002/mp.12431S46954707449Harris, R. I., & Macnab, I. (1954). STRUCTURAL CHANGES IN THE LUMBAR INTERVERTEBRAL DISCS. The Journal of Bone and Joint Surgery. British volume, 36-B(2), 304-322. doi:10.1302/0301-620x.36b2.304Oliveira, M. F. de, Rotta, J. M., & Botelho, R. V. (2015). Survival analysis in patients with metastatic spinal disease: the influence of surgery, histology, clinical and neurologic status. 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