183 research outputs found
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
Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks
This paper presents a method for automatic segmentation, localization, and
identification of vertebrae in arbitrary 3D CT images. Many previous works do
not perform the three tasks simultaneously even though requiring a priori
knowledge of which part of the anatomy is visible in the 3D CT images. Our
method tackles all these tasks in a single multi-stage framework without any
assumptions. In the first stage, we train a 3D Fully Convolutional Networks to
find the bounding boxes of the cervical, thoracic, and lumbar vertebrae. In the
second stage, we train an iterative 3D Fully Convolutional Networks to segment
individual vertebrae in the bounding box. The input to the second networks have
an auxiliary channel in addition to the 3D CT images. Given the segmented
vertebra regions in the auxiliary channel, the networks output the next
vertebra. The proposed method is evaluated in terms of segmentation,
localization, and identification accuracy with two public datasets of 15 3D CT
images from the MICCAI CSI 2014 workshop challenge and 302 3D CT images with
various pathologies introduced in [1]. Our method achieved a mean Dice score of
96%, a mean localization error of 8.3 mm, and a mean identification rate of
84%. In summary, our method achieved better performance than all existing works
in all the three metrics
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
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
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