978 research outputs found
MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images
The analysis of glandular morphology within colon histopathology images is an
important step in determining the grade of colon cancer. Despite the importance
of this task, manual segmentation is laborious, time-consuming and can suffer
from subjectivity among pathologists. The rise of computational pathology has
led to the development of automated methods for gland segmentation that aim to
overcome the challenges of manual segmentation. However, this task is
non-trivial due to the large variability in glandular appearance and the
difficulty in differentiating between certain glandular and non-glandular
histological structures. Furthermore, a measure of uncertainty is essential for
diagnostic decision making. To address these challenges, we propose a fully
convolutional neural network that counters the loss of information caused by
max-pooling by re-introducing the original image at multiple points within the
network. We also use atrous spatial pyramid pooling with varying dilation rates
for preserving the resolution and multi-level aggregation. To incorporate
uncertainty, we introduce random transformations during test time for an
enhanced segmentation result that simultaneously generates an uncertainty map,
highlighting areas of ambiguity. We show that this map can be used to define a
metric for disregarding predictions with high uncertainty. The proposed network
achieves state-of-the-art performance on the GlaS challenge dataset and on a
second independent colorectal adenocarcinoma dataset. In addition, we perform
gland instance segmentation on whole-slide images from two further datasets to
highlight the generalisability of our method. As an extension, we introduce
MILD-Net+ for simultaneous gland and lumen segmentation, to increase the
diagnostic power of the network.Comment: Initial version published at Medical Imaging with Deep Learning
(MIDL) 201
Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.
Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature
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