MILD‐Net: Minimal information loss dilated network for gland instance segmentation in colon histology images

By Simon Graham, Hao Chen, Qi-Yan Dou, P. Heng, Nasir M. Rajpoot. Medical Image Anal., 2018

366 citations recorded by the source corpus.

HighlightsAccurate gland instance segmentation in colon histology images using a novel minimal information loss dilated network.Uncertainty quantification using the concept of random transformation sampling.A variant of the proposed framework for simultaneous gland and lumen segmentation.Detailed experimentation and comparative analysis on two independent colorectal adenocarcinoma datasets.Perform gland instance segmentation on two further datasets containing whole‐slide images to demonstrate the extensibility of the proposed method. Graphical abstract Figure. No caption available. ABSTRACT 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‐NetSymbol for simultaneous gland and lumen segmentation, to increase the diagnostic power of the network. Symbol. No caption available.

Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/43b7f2450681e5ebcbcf772c8224ae9a2d8fe618

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, methodology): “A similar approach was followed by Graham et al. [203] for colon glands segmentation.”
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images (2020, methodology): “In the bottom part of Table II, we compare our proposed approach with MILD-Net [13] and Rota-Net [28], which are top-performing gland segmentation methods and therefore can be appropriately used for performance benchmarking.”
Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework (2020, background): “Most recently, Graham et al. [19] proposed a minimal information loss dilated network for gland instance segmentation.”