Browsing by Subject "Gland segmentation"

Now showing 1 - 4 of 4

Open Access
Automatic segmentation of colon glands using object-graphs
(Elsevier BV, 2010) Gunduz Demir, C.; Kandemir, M.; Tosun, A. B.; Sokmensuer, C.
Gland segmentation is an important step to automate the analysis of biopsies that contain glandular structures. However, this remains a challenging problem as the variation in staining, fixation, and sectioning procedures lead to a considerable amount of artifacts and variances in tissue sections, which may result in huge variances in gland appearances. In this work, we report a new approach for gland segmentation. This approach decomposes the tissue image into a set of primitive objects and segments glands making use of the organizational properties of these objects, which are quantified with the definition of object-graphs. As opposed to the previous literature, the proposed approach employs the object-based information for the gland segmentation problem, instead of using the pixel-based information alone. Working with the images of colon tissues, our experiments demonstrate that the proposed object-graph approach yields high segmentation accuracies for the training and test sets and significantly improves the segmentation performance of its pixel-based counterparts. The experiments also show that the object-based structure of the proposed approach provides more tolerance to artifacts and variances in tissues. © 2009 Elsevier B.V. All rights reserved.
Open Access
Boosting fully convolutional networks for gland instance segmentation in histopathological images
(2019-08) Güneşli, Gözde Nur
In the current literature, fully convolutional neural networks (FCNs) are the most preferred architectures for dense prediction tasks, including gland segmentation. However, a signi cant challenge is to adequately train these networks to correctly predict pixels that are hard-to-learn. Without additional strategies developed for this purpose, networks tend to learn poor generalizations of the dataset since the loss functions of the networks during training may be dominated by the most common and easy-to-learn pixels in the dataset. A typical example of this is the border separation problem in the gland instance segmentation task. Glands can be very close to each other, and since the border regions contain relatively few pixels, it is more di cult to learn these regions and separate gland instances. As this separation is essential for the gland instance segmentation task, this situation arises major drawbacks on the results. To address this border separation problem, it has been proposed to increase the given attention to border pixels during network training either by increasing the relative loss contribution of these pixels or by adding border detection as an additional task to the architecture. Although these techniques may help better separate gland borders, there may exist other types of hard-to-learn pixels (and thus, other mistake types), mostly related to noise and artifacts in the images. Yet, explicitly adjusting the appropriate attention to train the networks against every type of mistake is not feasible. Motivated by this, as a more e ective solution, this thesis proposes an iterative attention learning model based on adaptive boosting. The proposed AttentionBoost model is a multi-stage dense segmentation network trained directly on image data without making any prior assumption. During the end-to-end training of this network, each stage adjusts the importance of each pixel-wise prediction for each image depending on the errors of the previous stages. This way, each stage learns the task with di erent attention forcing the stage to learn the mistakes of the earlier stages. With experiments on the gland instance segmentation task, we demonstrate that our model achieves better segmentation results than the approaches in the literature.
Open Access
Segmentation of colon glands by object graphs
(2008) Kandemir, Melih
Histopathological examination is the most frequently used technique for clinical diagnosis of a large group of diseases including cancer. In order to reduce the observer variability and the manual effort involving in this visual examination, many computational methods have been proposed. These methods represent a tissue with a set of mathematical features and use these features in further analysis of the biopsy. For the tissue types that contain glandular structures, one of these analyses is to examine the changes in these glandular structures. For such analyses, the very first step is to segment the tissue into its glands. In this thesis, we present an object-based method for the segmentation of colon glands. In this method, we propose to decompose the image into a set of primitive objects and use the spatial distribution of these objects to determine the locations of glands. In the proposed method, pixels are first clustered into different histological structures with respect to their color intensities. Then, the clustered image is decomposed into a set of circular primitive objects (white objects for luminal regions and black objects for nuclear regions) and a graph is constructed on these primitive objects to quantify their spatial distribution. Next, the features are extracted from this graph and these features are used to determine the seed points of gland candidates. Starting from these seed points, the inner glandular regions are grown considering the locations of black objects. Finally, false glands are eliminated based on another set of features extracted from the identified inner regions and exact boundaries of the remaining true glands are determined considering the black objects that are located near the inner glandular regions. Our experiments on the images of colon biopsies have demonstrated that our proposed method leads to high sensitivity, specificity, and accuracy rates.and that it greatly improves the performance of the previous pixel-based gland segmentation algorithms. Our experiments have also shown that the object-based structure of the method provides tolerance to artifacts resulting from variances in biopsy staining and sectioning procedures. This proposed method offers an infrastructure for further analysis of glands for the purpose of automated cancer diagnosis and grading.
Open Access
Tissue object patterns for segmentation in histopathological images
(ACM, 2011) Gündüz-Demir, Çiğdem
In the current practice of medicine, histopathological examination is the gold standard for routine clinical diagnosis and grading of cancer. However, as this examination involves the visual analysis of biopsies, it is subject to a considerable amount of observer variability. In order to decrease the variability, it has been proposed to develop systems that mathematically model the histopathological tissue images and automate the analysis. Segmentation constitutes the first step for most of these automated systems. Nevertheless, the segmentation in histopathological images remains a challenging task since these images typically show variances due to their complex nature and may include a large amount of noise and artifacts due to the tissue preparation procedures. In our research group, we recently developed different segmentation algorithms that rely on representing a tissue image with a set of tissue objects and using the structural pattern of these objects in segmentation. In this paper, we review these segmentation algorithms, discussing their clinical demonstrations on colon tissues. © 2011 ACM.