Automated segmentation of vertebral bone from diagnostic Computed Tomography (CT) images has become an important part of clinical workflow today. There is an increasing need for computer aided diagnosis applications of various spine disorders including scoliosis, fracture detection and even automated reporting. While modelbased methods have been widely used, recent deep Learning methods have shown a great potential in this area. However, choice of optimal configuration of the network to get the best segmentation performance is challenging. In this work, we explore the impact of different training and inference options, including dimensions, activation function, batch normalization, kernel size, filters, patch size and patch selection strategy in U-Net architecture. 20 publicly available CT Spine datasets from Spineweb repository was used in this study divided into training/test datasets. Training with different DL configurations were repeated with these datasets. We used the best weights corresponding to each configuration for inference on the independent test dataset. These results on the test dataset with the best weights for each configurations were compared. 3D models performed consistently better than 2D approaches. Overlapped patch based inference had a big impact on enhancing performance accuracy. The selection of training patch size was also found to be crucial in improving the model performance. Moreover, the need for an effective balance of positive and negative training patches was found. The best performance in our study was obtained by using overlapped patch inference, training with RELU activation and batch normalization in a 3D U-Net architecture with training patch size of 128×128×32 that resulted in average values of precision= 97%, sensitivity= 96% and F1 (Dice)= 96% for the test dataset.
Parsing volumetric computed tomography (CT) into 10 or more salient organs simultaneously is a challenging task with many applications such as personalized scan planning and dose reporting. In the clinic, pre-scan data can come in the form of very low dose volumes acquired just prior to the primary scan or from an existing primary scan. To localize organs in such diverse data, we propose a new learning based framework that we call hierarchical pictorial structures (HPS) which builds multiple levels of models in a tree-like hierarchy that mirrors the natural decomposition of human anatomy from gross structures to finer structures. Each node of our hierarchical model learns (1) the local appearance and shape of structures, and (2) a generative global model that learns probabilistic, structural arrangement. Our main contribution is twofold. First we embed the pictorial structures approach in a hierarchical framework which reduces test time image interpretation and allows for the incorporation of additional geometric constraints that robustly guide model fitting in the presence of noise. Second we guide our HPS framework with the probabilistic cost maps extracted using random decision forests using volumetric 3D HOG features which makes our model fast to train and fast to apply to novel test data and posses a high degree of invariance to shape distortion and imaging artifacts. All steps require approximate 3 mins to compute and all organs are located with suitably high accuracy for our clinical applications such as personalized scan planning for radiation dose reduction. We assess our method using a database of volumetric CT scans from 81 subjects with widely varying age and pathology and with simulated ultra-low dose cadaver pre-scan data.
Tissue characterization from imaging studies is an integral part of clinical practice. We describe a spectral filter
design for tissue separation in dual energy CT scans obtained from Gemstone Spectral Imaging scanner. It
enables to have better 2D/3D visualization and tissue characterization in normal and pathological conditions.
The major challenge to classify tissues in conventional computed tomography (CT) is the x-ray attenuation
proximity of multiple tissues at any given energy. The proposed method analyzes the monochromatic images
at different energy levels, which are derived from the two scans obtained at low and high KVp through fast
switching. Although materials have a distinct attenuation profile across different energies, tissue separation
is not trivial as tissues are a mixture of different materials with range of densities that vary across subjects.
To address this problem, we define spectral filtering, that generates probability maps for each tissue in multi-energy
space. The filter design incorporates variations in the tissue due to composition, density of individual
constituents and their mixing proportions. In addition, it also provides a framework to incorporate zero mean
Gaussian noise. We demonstrate the application of spectral filtering for bone-free vascular visualization and
Pulmonary fissures separate human lungs into five distinct regions called lobes. Detection of fissure is essential
for localization of the lobar distribution of lung diseases, surgical planning and follow-up. Treatment planning
also requires calculation of the lobe volume. This volume estimation mandates accurate segmentation of the
fissures. Presence of other structures (like vessels) near the fissure, along with its high variational probability in
terms of position, shape etc. makes the lobe segmentation a challenging task. Also, false incomplete fissures and
occurrence of diseases add to the complications of fissure detection. In this paper, we propose a semi-automated
fissure segmentation algorithm using a minimal path approach on CT images. An energy function is defined such
that the path integral over the fissure is the global minimum. Based on a few user defined points on a single slice
of the CT image, the proposed algorithm minimizes a 2D energy function on the sagital slice computed using (a)
intensity (b) distance of the vasculature, (c) curvature in 2D, (d) continuity in 3D. The fissure is the infimum
energy path between a representative point on the fissure and nearest lung boundary point in this energy domain.
The algorithm has been tested on 10 CT volume datasets acquired from GE scanners at multiple clinical sites.
The datasets span through different pathological conditions and varying imaging artifacts.
Proc. SPIE. 7259, Medical Imaging 2009: Image Processing
KEYWORDS: Image processing algorithms and systems, Detection and tracking algorithms, Image segmentation, Image processing, Image registration, Medical imaging, Computed tomography, Current controlled current source
Automated labeling of the bronchial tree is essential for localization of airway related diseases (e.g. chronic bronchitis) and is also a useful precursor to lung-lobe labeling. We describe an automated method for registration-based labeling of a bronchial tree. The bronchial tree is segmented from a CT image using a region-growing based algorithm. The medial line of the extracted tree is then computed using a potential field based approach. The expert-labeled target (atlas) and the source bronchial trees in the form of extracted centerline point sets are brought into alignment by calculating a non-rigid thin-plate spline (TPS) mapping from the source to the target. The registration takes into account global as well as local variations in anatomy between the two images through the use of separable linear and non-linear components of the transformation; as a result it is well suited to matching structures that deviate at finer levels: namely higher order branches. The method is validated by registering together pairs of datasets for which the ground truth labels are known in advance: the labels are transferred after matching target to source and then compared with the true values. The method was tested on datasets each containing 18 branch centerpoints and 12 bifurcation locations (30 landmarks in total) annotated manually by a radiologist, where the performance was measured as the number of landmarks having the correct transfer of labels. An overall accuracy of labeling of 91.5 % was obtained in matching 23 pairs of datasets obtained from different patients.