Query: two step segmentation

title: A fast, weighted CRF algorithm based on a two step superpixel generation for SAR image segmentation
abstract: Among the Markov random field methods, conditional random fields (CRF) model has shown good results in Synthetic Aperture Radar (SAR) image segmentation. CRF not only directly models the posterior distribution of label field conditioned on images but also allows the interactions between observations. In this paper, we propose a Fast, Weighted CRF (FWCRF) algorithm based on a two step superpixel generation method. In our method, heterogeneity of intensity in SAR images due to speckle noise and backscattering is considered. The first step is a preprocessing task for suppressing speckle noise which is done by the L 0 smoothing algorithm. It is followed by Sobel edge detector to highlight heterogeneous regions and edges. Then SAR image is partitioned into homogeneous and heterogeneous regions by using fast robust fuzzy c means clustering (FRFCM) Simultaneously, the simple linear iterative clustering (SLIC) algorithm is applied to split the image into superpixels. Next, the superpixels belonging to the same class are merged by using the Density Based Spatial Clustering of Applications with Noise (DBSCAN) method. Finally, the SAR image is labelled using the FWCRF algorithm which consists of weighted pairwise potential based on adaptive features and applying FRFCM as an initial segmentation method. The proposed algorithm is evaluated in comparison with other conditional random field schemes. The results of our proposed method demonstrate accuracy improvement in segmentation.

title: Two step deep neural network for segmentation of deep white matter hyperintensities in migraineurs
abstract: Patients with migraine show an increased presence of white matter hyperintensities (WMHs) especially deep WMHs. Segmentation of small, deep WMHs is a critical issue in managing migraine care. Here, we aim to develop a novel approach to segmenting deep WMHs using deep neural networks based on the U Net.  148 non elderly subjects with migraine were recruited for this study. Our model consists of two networks: the first identifies potential deep WMH candidates, and the second reduces the false positives within the candidates. The first network for initial segmentation includes four down sampling layers and four up sampling layers to sort the candidates. The second network for false positive reduction uses a smaller field of view and depth than the first network to increase utilization of local information.  Our proposed model segments deep WMHs with a high true positive rate of 0.88, a low false discovery rate of 0.13, and F1 score of 0.88 tested with ten fold cross validation. Our model was automatic and performed better than existing models based on conventional machine learning.  We developed a novel segmentation framework tailored for deep WMHs using U Net. Our algorithm is open access to promote future research in quantifying deep WMHs and might contribute to the effective management of WMHs in migraineurs.

title: Two step convolutional neural network for automatic glottis localization and segmentation in stroboscopic videos.
abstract: Precise analysis of the vocal fold vibratory pattern in a stroboscopic video plays a key role in the evaluation of voice disorders. Automatic glottis segmentation is one of the preliminary steps in such analysis. In this work, it is divided into two subproblems namely, glottis localization and glottis segmentation. A two step convolutional neural network (CNN) approach is proposed for the automatic glottis segmentation. Data augmentation is carried out using two techniques 1) Blind rotation (WB) 2) Rotation with respect to glottis orientation (WO) The dataset used in this study contains stroboscopic videos of 18 subjects with Sulcus vocalis, in which the glottis region is annotated by three speech language pathologists (SLPs) The proposed two step CNN approach achieves an average localization accuracy of 90.08% and a mean dice score of 0.65.

title: Two step convolutional neural network for automatic glottis localization and segmentation in stroboscopic videos
abstract: Precise analysis of the vocal fold vibratory pattern in a stroboscopic video plays a key role in the evaluation of voice disorders. Automatic glottis segmentation is one of the preliminary steps in such analysis. In this work, it is divided into two subproblems namely, glottis localization and glottis segmentation. A two step convolutional neural network (CNN) approach is proposed for the automatic glottis segmentation. Data augmentation is carried out using two techniques 1) Blind rotation (WB) 2) Rotation with respect to glottis orientation (WO) The dataset used in this study contains stroboscopic videos of 18 subjects with Sulcus vocalis, in which the glottis region is annotated by three speech language pathologists (SLPs) The proposed two step CNN approach achieves an average localization accuracy of 90.08% and a mean dice score of 0.65.

title: Segmentation of Coronary Calcified Plaque in Intravascular OCT Images Using a Two Step Deep Learning Approach
abstract: We developed a fully automated, two step deep learning approach for characterizing coronary calcified plaque in intravascular optical coherence tomography (IVOCT) images. First, major calcification lesions were detected from an entire pullback using a 3D convolutional neural network (CNN) Second, a SegNet deep learning model with the Tversky loss function was used to segment calcified plaques in the major calcification lesions. The fully connected conditional random field and the frame interpolation of the missing calcification frames were used to reduce classification errors. We trained/tested the networks on a large dataset comprising 8,231 clinical images from 68 patients with 68 vessels and 4,320 ex vivo cadaveric images from 4 hearts with 4 vessels. The 3D CNN model detected major calcifications with high sensitivity (97.7% specificity (87.7% and F1 score (0.922) Compared to the standard one step approach, our two step deep learning approach significantly improved sensitivity (from 77.5% to 86.2% precision (from 73.5% to 75.8% and F1 score (from 0.749 to 0.781) We investigated segmentation performance for varying numbers of training samples; at least 3,900 images were required to obtain stable segmentation results. We also found very small differences in calcification attributes (e.g. angle, thickness, and depth) and identical calcium scores on repetitive pullbacks, indicating excellent reproducibility. Applied to new clinical pullbacks, our method has implications for real time treatment planning and imaging research.

title: Estimating Conformational Traits in Dairy Cattle With DeepAPS: A Two Step Deep Learning Automated Phenotyping and Segmentation Approach
abstract: Assessing conformation features in an accurate and rapid manner remains a challenge in the dairy industry. While recent developments in computer vision has greatly improved automated background removal, these methods have not been fully translated to biological studies. Here, we present a composite method (DeepAPS) that combines two readily available algorithms in order to create a precise mask for an animal image. This method performs accurately when compared with manual classification of proportion of coat color with an adjusted R2 0.926. Using the output mask, we are able to automatically extract useful phenotypic information for 14 additional morphological features. Using pedigree and image information from a web catalog (www.semex.com) we estimated high heritabilities (ranging from h2 0.18 0.82) indicating that meaningful biological information has been extracted automatically from imaging data. This method can be applied to other datasets and requires only a minimal number of image annotations ~50) to train this partially supervised machine learning approach. DeepAPS allows for the rapid and accurate quantification of multiple phenotypic measurements while minimizing study cost. The pipeline is available at https:/github.com/lauzingaretti/deepaps.

title: Automated pavement crack detection and segmentation based on two step convolutional neural network

title: A two step registration classification approach to automated segmentation of multimodal images for high throughput greenhouse plant phenotyping
abstract: Automated segmentation of large amount of image data is one of the major bottlenecks in high throughput plant phenotyping. Dynamic optical appearance of developing plants, inhomogeneous scene illumination, shadows and reflections in plant and background regions complicate automated segmentation of unimodal plant images. To overcome the problem of ambiguous color information in unimodal data, images of different modalities can be combined to a virtual multispectral cube. However, due to motion artefacts caused by the relocation of plants between photochambers the alignment of multimodal images is often compromised by blurring artifacts.  Here, we present an approach to automated segmentation of greenhouse plant images which is based on co registration of fluorescence (FLU) and of visible light (VIS) camera images followed by subsequent separation of plant and marginal background regions using different species and camera view tailored classification models. Our experimental results including a direct comparison with manually segmented ground truth data show that images of different plant types acquired at different developmental stages from different camera views can be automatically segmented with the average accuracy of $93\ 93 $SD=5\ S D 5 using our two step registration classification approach.  Automated segmentation of arbitrary greenhouse images exhibiting highly variable optical plant and background appearance represents a challenging task to data classification techniques that rely on detection of invariances. To overcome the limitation of unimodal image analysis, a two step registration classification approach to combined analysis of fluorescent and visible light images was developed. Our experimental results show that this algorithmic approach enables accurate segmentation of different FLU/VIS plant images suitable for application in fully automated high throughput manner.

title: Two Step CNN Framework for Text Line Recognition in Camera Captured Images
abstract: In this paper, we introduce an "on the device" text line recognition framework that is designed for mobile or embedded systems. We consider per character segmentation as a language independent problem and individual character recognition as a language dependent one. Thus, the proposed solution is based on two separate artificial neural networks (ANN) and dynamic programming instead of employing image processing methods for the segmentation step or end to end ANN. To satisfy the tight constraints on memory size imposed by embedded systems and to avoid overfitting, we employ ANNs with a small number of trainable parameters. The primary purpose of our framework is the recognition of low quality images of identity documents with complex backgrounds and a variety of languages and fonts. We demonstrate that our solution shows high recognition accuracy on natural datasets even being trained on purely synthetic data. We use MIDV 500 and Census 1961 Project datasets for text line recognition. The proposed method considerably surpasses the algorithmic method implemented in Tesseract 3.05, the LSTM method (Tesseract 4.00) and unpublished method used in the ABBYY FineReader 15 system. Also, our framework is faster than other compared solutions. We show the language independence of our segmenter with the experiment with Cyrillic, Armenian, and Chinese text lines.

title: Multi Step Segmentation Algorithm for Quantitative Magnetic Resonance Imaging T2 Mapping of Ruptured Achilles Tendons
abstract: Diagnosis and treatment monitoring of Achilles tendon ruptures (ATRs) are supported by medical imaging, in particular by magnetic resonance imaging (MRI) for tendon volume and healing assessment. Therefore, we propose an automatic, multi step segmentation algorithm for quantitative MRI T2 mapping of ATRs. Seventy retrospective post trauma, post surgery and follow up studies were included in this research. The automatic segmentation algorithm for the inhomogeneous, noisy Achilles tendon region consisted of a multi step anisotropic denoising, T2 map reconstruction with a weighted log linear regression, thresholding with T2 time parameters, region growing and morphological closing. The automatic segmentation results were compared with those from manual contour tracing (MCT) performed by two radiologists. The Intersection over Union (IoU) specificity, sensitivity, F1 score, Yasnoff's normalized distance (YND) and type I and II errors were used to assess the segmentation accuracy. The segmentation methods were also compared with a Bland Altman plot of the volumes of the segmented regions, with mean differences, correlation coefficients and 95% confidence intervals. The mean specificity and sensitivity values were high, 99.8+ 0.1% and 85.9+ 8.7% respectively, with corresponding type I and II errors of 0.2+ 0.1% and 14.1+ 8.7% The IoU, F1 score and YND were 71.0+ 9.2% 82.7+ 6.3% and 0.007+ 0.007% respectively. The tendon volumes obtained by manual and automatic segmentation were strongly positively correlated \text{R}{2} =0.85$ and the Bland Altman plot depicted good comparability. The average difference was 28 voxels (95% confidence interval: 2726 to 2782 voxels) For ATRs, our method is reliable, with a strong positive correlation with MCT and a very high specificity.