7 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
MRI-SegFlow: a novel unsupervised deep learning pipeline enabling accurate vertebral segmentation of MRI images.
Most deep learning based vertebral segmentation methods require laborious manual labelling tasks. We aim to establish an unsupervised deep learning pipeline for vertebral segmentation of MR images. We integrate the sub-optimal segmentation results produced by a rule-based method with a unique voting mechanism to provide supervision in the training process for the deep learning model. Preliminary validation shows a high segmentation accuracy achieved by our method without relying on any manual labelling.The clinical relevance of this study is that it provides an efficient vertebral segmentation method with high accuracy. Potential applications are in automated pathology detection and vertebral 3D reconstructions for biomechanical simulations and 3D printing, facilitating clinical decision making, surgical planning and tissue engineering
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
Deep Learning based 3D Segmentation: A Survey
3D object segmentation is a fundamental and challenging problem in computer
vision with applications in autonomous driving, robotics, augmented reality and
medical image analysis. It has received significant attention from the computer
vision, graphics and machine learning communities. Traditionally, 3D
segmentation was performed with hand-crafted features and engineered methods
which failed to achieve acceptable accuracy and could not generalize to
large-scale data. Driven by their great success in 2D computer vision, deep
learning techniques have recently become the tool of choice for 3D segmentation
tasks as well. This has led to an influx of a large number of methods in the
literature that have been evaluated on different benchmark datasets. This paper
provides a comprehensive survey of recent progress in deep learning based 3D
segmentation covering over 150 papers. It summarizes the most commonly used
pipelines, discusses their highlights and shortcomings, and analyzes the
competitive results of these segmentation methods. Based on the analysis, it
also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure