Object detection in medical images using deep learning is a challenging task, due to the imbalance often present in the data. Deep learning algorithms require large amount of balanced data to achieve optimal performance, as well as close monitoring and ne-tuning of hyper parameters. For most applications, such performance monitoring is done by simply feeding unseen data trough the network, and then using the loss function for evaluation. In the case of small or sparse objects, the loss function might not able to describe the features needed, but such features can be hard to capture in a loss function. In this paper we introduce a lesion-wise whole volume validation tool, which allows more a more accurate performance monitoring of segmentation of small and sparse objects. We showcase the efficacy of our tool by applying it to the task of microbleed segmentation, and compare the behaviour of lesionwise-whole volume validation compared to well known segmentation loss functions. Microbleeds are visible as small (less than 10 mm), ovoid hypo-intensities on T2*-weighted and susceptibility weighted magnetic resonance images. Detection of microbleeds is clinically relevant, as microbleeds can indicate the risk of recurrent stroke, and are used as imaging biomarker for various neurodegenerative diseases. Manual detection or segmentation is time consuming and error prone, and suffers from high inter- and intraobserver variability. Due to the sparsity and small size of the lesions, the data is severely imbalanced.
KEYWORDS: Image segmentation, Brain, Convolutional neural networks, Neuroimaging, Medical imaging, Monte Carlo methods, Magnetic resonance imaging, Statistical analysis, Image registration, Data modeling
For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input. To induce invariance in CNNs to such transformations, we propose Probabilistic Augmentation of Data using Diffeomorphic Image Transformation (PADDIT) – a systematic framework for generating realistic transformations that can be used to augment data for training CNNs. The main advantage of PADDIT is the ability to produce transformations that capture the morphological variability in the training data. To this end, PADDIT constructs a mean template which represents the main shape tendency of the training data. A Hamiltonian Monte Carlo(HMC) scheme is used to sample transformations which warp the training images to the generated mean template. Augmented images are created by warping the training images using the sampled transformations. We show that CNNs trained with PADDIT outperforms CNNs trained without augmentation and with generic augmentation (0.2 and 0.15 higher dice accuracy respectively) in segmenting white matter hyperintensities from T1 and FLAIR brain MRI scans.
Brain atrophy from structural magnetic resonance images (MRIs) is widely used as an imaging surrogate marker for Alzheimers disease. Their utility has been limited due to the large degree of variance and subsequently high sample size estimates. The only consistent and reasonably powerful atrophy estimation methods has been the boundary shift integral (BSI). In this paper, we first propose a tensor-based morphometry (TBM) method to measure voxel-wise atrophy that we combine with BSI. The combined model decreases the sample size estimates significantly when compared to BSI and TBM alone.
Obtaining regional volume changes from a deformation field is more precise when using simplex counting (SC) compared with Jacobian integration (JI) due to the numerics involved in the latter. Although SC has been proposed before, numerical properties underpinning the method and a thorough evaluation of the method against JI is missing in the literature. The contributions of this paper are: (a) we propose surface propagation (SP)—a simplification to SC that significantly reduces its computational complexity; (b) we will derive the orders of approximation of SP which can also be extended to SC. In the experiments, we will begin by empirically showing that SP is indeed nearly identical to SC, and that both methods are more stable than JI in presence of moderate to large deformation noise. Since SC and SP are identical, we consider SP as a representative of both the methods for a practical evaluation against JI. In a real application on Alzheimer’s disease neuroimaging initiative data, we show the following: (a) SP produces whole brain and medial temporal lobe atrophy numbers that are significantly better than JI at separating between normal controls and Alzheimer’s disease patients; (b) SP produces disease group atrophy differences comparable to or better than those obtained using FreeSurfer, demonstrating the validity of the obtained clinical results. Finally, in a reproducibility study, we show that the voxel-wise application of SP yields significantly lower variance when compared to JI.
Interpolating kernels are crucial to solving a stationary velocity field (SVF) based image registration problem. This is because, velocity fields need to be computed in non-integer locations during integration. The regularity in the solution to the SVF registration problem is controlled by the regularization term. In a variational formulation, this term is traditionally expressed as a squared norm which is a scalar inner product of the interpolating kernels parameterizing the velocity fields. The minimization of this term using the standard spline interpolation kernels (linear or cubic) is only approximative because of the lack of a compatible norm. In this paper, we propose to replace such interpolants with a norm-minimizing interpolant - the Wendland kernel which has the same computational simplicity like B-Splines. An application on the Alzheimer's disease neuroimaging initiative showed that Wendland SVF based measures separate (Alzheimer's disease v/s normal controls) better than both B-Spline SVFs (p<0.05 in amygdala) and B-Spline freeform deformation (p<0.05 in amygdala and cortical gray matter).
This work investigates a novel way of looking at the regions in the brain and their relationship as possible markers to classify normal control (NC), mild cognitive impaired (MCI), and Alzheimer Disease (AD) subjects. MRI scans from a subset of 101 subjects from the ADNI study at baseline was used for this study. 40 regions in the brain including hippocampus, amygdala, thalamus, white, and gray matter were segmented using FreeSurfer. From this data, we calculated the distance between the center of mass of each region, the normalized number of voxels and the percentage volume and surface connectivity shared between the regions. These markers were used for classification using a linear discriminant analysis in a leave-one-out manner. We found that the percentage of surface and volume connectivity between regions gave a significant classification between NC and AD and borderline significant between MCI and AD even after correction for whole brain volume at baseline. The results show that the morphometric connectivity markers include more information than whole brain volume or distance markers. This suggests that one can gain additional information by combining morphometric connectivity markers with traditional volume and shape markers.
KEYWORDS: Magnetic resonance imaging, Spine, Image processing, Pathology, Image segmentation, Image quality, Image processing algorithms and systems, Medical imaging, Computer aided diagnosis and therapy, Current controlled current source
The identification of key landmark points in an MR spine image is an important step for tasks such as
vertebra counting. In this paper, we propose a template matching based approach for automatic detection of two key
landmark points, namely the second cervical vertebra (C2) and the sacrum from sagittal MR images. The approach
is comprised of an approximate localization of vertebral column followed by matching with appropriate templates in
order to detect/localize the landmarks. A straightforward extension of the work described here is an automated
classification of spine section(s). It also serves as a useful building block for further automatic processing such as
extraction of regions of interest for subsequent image processing and also in aiding the counting of vertebra.
Mammography reading by radiologists and breast tissue image interpretation by pathologists often leads to high False
Positive (FP) Rates. Similarly, current Computer Aided Diagnosis (CADx) methods tend to concentrate more on
sensitivity, thus increasing the FP rates. A novel method is introduced here which employs similarity based method to
decrease the FP rate in the diagnosis of microcalcifications. This method employs the Principal Component Analysis
(PCA) and the similarity metrics in order to achieve the proposed goal. The training and testing set is divided into
generalized (Normal and Abnormal) and more specific (Abnormal, Normal, Benign) classes. The performance of this
method as a standalone classification system is evaluated in both the cases (general and specific). In another approach
the probability of each case belonging to a particular class is calculated. If the probabilities are too close to classify, the
augmented CADx system can be instructed to have a detailed analysis of such cases. In case of normal cases with high
probability, no further processing is necessary, thus reducing the computation time. Hence, this novel method can be
employed in cascade with CADx to reduce the FP rate and also avoid unnecessary computational time. Using this
methodology, a false positive rate of 8% and 11% is achieved for mammography and cellular images respectively.
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