2,830 research outputs found
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
Accurate and fast extraction of lung volumes from computed tomography (CT)
scans remains in a great demand in the clinical environment because the
available methods fail to provide a generic solution due to wide anatomical
variations of lungs and existence of pathologies. Manual annotation, current
gold standard, is time consuming and often subject to human bias. On the other
hand, current state-of-the-art fully automated lung segmentation methods fail
to make their way into the clinical practice due to their inability to
efficiently incorporate human input for handling misclassifications and praxis.
This paper presents a lung annotation tool for CT images that is interactive,
efficient, and robust. The proposed annotation tool produces an "as accurate as
possible" initial annotation based on the fuzzy-connectedness image
segmentation, followed by efficient manual fixation of the initial extraction
if deemed necessary by the practitioner. To provide maximum flexibility to the
users, our annotation tool is supported in three major operating systems
(Windows, Linux, and the Mac OS X). The quantitative results comparing our free
software with commercially available lung segmentation tools show higher degree
of consistency and precision of our software with a considerable potential to
enhance the performance of routine clinical tasks.Comment: 4 pages, 6 figures; to appear in the proceedings of 36th Annual
International Conference of the IEEE Engineering in Medicine and Biology
Society (EMBC 2014
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A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer.
Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required
Highly accurate model for prediction of lung nodule malignancy with CT scans
Computed tomography (CT) examinations are commonly used to predict lung
nodule malignancy in patients, which are shown to improve noninvasive early
diagnosis of lung cancer. It remains challenging for computational approaches
to achieve performance comparable to experienced radiologists. Here we present
NoduleX, a systematic approach to predict lung nodule malignancy from CT data,
based on deep learning convolutional neural networks (CNN). For training and
validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort.
All nodules were identified and classified by four experienced thoracic
radiologists who participated in the LIDC project. NoduleX achieves high
accuracy for nodule malignancy classification, with an AUC of ~0.99. This is
commensurate with the analysis of the dataset by experienced radiologists. Our
approach, NoduleX, provides an effective framework for highly accurate nodule
malignancy prediction with the model trained on a large patient population. Our
results are replicable with software available at
http://bioinformatics.astate.edu/NoduleX
CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans
Segmentation of lung tissue in computed tomography (CT) images is a precursor
to most pulmonary image analysis applications. Semantic segmentation methods
using deep learning have exhibited top-tier performance in recent years,
however designing accurate and robust segmentation models for lung tissue is
challenging due to the variations in shape, size, and orientation.
Additionally, medical image artifacts and noise can affect lung tissue
segmentation and degrade the accuracy of downstream analysis. The practicality
of current deep learning methods for lung tissue segmentation is limited as
they require significant computational resources and may not be easily
deployable in clinical settings. This paper presents a fully automatic method
that identifies the lungs in three-dimensional (3D) pulmonary CT images using
deep networks and transfer learning. We introduce (1) a novel 2.5-dimensional
image representation from consecutive CT slices that succinctly represents
volumetric information and (2) a U-Net architecture equipped with pre-trained
InceptionV3 blocks to segment 3D CT scans while maintaining the number of
learnable parameters as low as possible. Our method was quantitatively assessed
using one public dataset, LUNA16, for training and testing and two public
datasets, namely, VESSEL12 and CRPF, only for testing. Due to the low number of
learnable parameters, our method achieved high generalizability to the unseen
VESSEL12 and CRPF datasets while obtaining superior performance over Luna16
compared to existing methods (Dice coefficients of 99.7, 99.1, and 98.8 over
LUNA16, VESSEL12, and CRPF datasets, respectively). We made our method publicly
accessible via a graphical user interface at medvispy.ee.kntu.ac.ir
Accuracy of Patient-Specific Organ Dose Estimates Obtained Using an Automated Image Segmentation Algorithm
The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was -7%, with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors
Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate
An algorithm was developed to segment solid pulmonary nodules attached to the chest wall in computed
tomography scans. The pleural surface was estimated and used to segment the nodule from the
chest wall. To estimate the surface, a robust approach was used to identify points that lie on the pleural
surface but not on the nodule. A 3D surface was estimated from the identified surface points. The
segmentation performance of the algorithm was evaluated on a database of 150 solid juxtapleural pulmonary
nodules. Segmented images were rated on a scale of 1 to 4 based on visual inspection, with 3 and
4 considered acceptable. This algorithm offers a large improvement in the success rate of juxtapleural
nodule segmentation, successfully segmenting 98.0% of nodules compared to 81.3% for a previously published
plane-fitting algorithm, which will provide for the development of more robust automated nodule
measurement methods
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