Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, there is a strong demand for the development of computer based image analysis systems. In this work, the focus is on the segmentation of the glomeruli constituting a highly relevant structure in renal histopathology, which has not been investigated before in combination with CNNs. We propose two different CNN cascades for segmentation applications with sparse objects. These approaches are applied to the problem of glomerulus segmentation and compared with conventional fully-convolutional networks. Overall, with the best performing cascade approach, single CNNs are outperformed and a pixel-level Dice similarity coefficient of 0.90 is obtained. Combined with qualitative and further object-level analyses the obtained results are assessed as excellent also compared to recent approaches. In conclusion, we can state that especially one of the proposed cascade networks proved to be a highly powerful tool for segmenting the renal glomeruli providing best segmentation accuracies and also keeping the computing time at a low level.
To circumvent the need for large annotated training data, @cite_16 proposed an unsupervised segmentation algorithm. The authors aim at segmenting the Bowman's capsule surrounding the glomerulus by applying a color-based segmentation approach. The segmentation method consists of k-means clustering after thresholding including morphological operations. The inherent problem here is, that the Bowman's capsule is not always visible, depending on the staining protocol as well as the cutting plane (Fig. , ). Therefore, this method is not capable of generating a reliable precise segmentation of all objects.
@cite_16
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Training a fully convolutional network for pixel-wise (or voxel-wise) image segmentation normally requires a large number of training images with corresponding ground truth label maps. However, it is a challenge to obtain such a large training set in the medical imaging domain, where expert annotations are time-consuming and difficult to obtain. In this paper, we propose a semi-supervised learning approach, in which a segmentation network is trained from both labelled and unlabelled data. The network parameters and the segmentations for the unlabelled data are alternately updated. We evaluate the method for short-axis cardiac MR image segmentation and it has demonstrated a high performance, outperforming a baseline supervised method. The mean Dice overlap metric is 0.92 for the left ventricular cavity, 0.85 for the myocardium and 0.89 for the right ventricular cavity. It also outperforms a state-of-the-art multi-atlas segmentation method by a large margin and the speed is substantially faster. Quantitative magnetic resonance analysis often requires accurate, robust, and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert manual segmentations as priors to achieve this task. Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation. Validation with two different datasets is presented. In our experiments, the hippocampi of 80 healthy subjects and the lateral ventricles of 80 patients with Alzheimer's disease were segmented. The influence on segmentation accuracy of different parameters such as patch size and number of training subjects was also studied. A comparison with an appearance-based method and a template-based method was also carried out. The highest median kappa index values obtained with the proposed method were 0.884 for hippocampus segmentation and 0.959 for lateral ventricle segmentation. Surgical tool segmentation in endoscopic images is the first step towards pose estimation and (sub-)task automation in challenging minimally invasive surgical operations. While many approaches in the literature have shown great results using modern machine learning methods such as convolutional neural networks, the main bottleneck lies in the acquisition of a large number of manually-annotated images for efficient learning. This is especially true in surgical context, where patient-to-patient differences impede the overall generalizability. In order to cope with this lack of annotated data, we propose a self-supervised approach in a robot-assisted context. To our knowledge, the proposed approach is the first to make use of the kinematic model of the robot in order to generate training labels. The core contribution of the paper is to propose an optimization method to obtain good labels for training despite an unknown hand-eye calibration and an imprecise kinematic model. The labels can subsequently be used for fine-tuning a fully-convolutional neural network for pixel-wise classification. As a result, the tool can be segmented in the endoscopic images without needing a single manually-annotated image. Experimental results on phantom and in vivo datasets obtained using a flexible robotized endoscopy system are very promising. In this work, we address the problem of performing class specific unsupervised object segmentation, i.e., automatic segmentation without annotated training images. We propose a hybrid graph model (HGM) to integrate recognition and segmentation into a unified process. The vertices of a hybrid graph represent the entities associated to the object class or local image features. The vertices are connected by directed edges and or undirected ones, which represent the dependence between the shape priors of the class (for recognition) and the similarity between the color texture priors within an image (for segmentation), respectively. By simultaneously considering the Markov chain formed by the directed subgraph and the minimal cut of the undirected subgraph, the likelihood that the vertices belong to the underlying class can be computed. Given a set of images each containing objects of the same class, our HGM based method automatically identifies in each image the area that the objects occupy. Experiments on 14 sets of images show promising results.