MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images

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-Net+ for simultaneous gland and lumen segmentation, to increase the diagnostic power of the network.

Initial version published at Medical Imaging with Deep Learning (MIDL) 2018

References in corpus (11)

• A Survey on Deep Learning in Medical Image Analysis
• TensorFlow: A system for large-scale machine learning
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• Fully Convolutional Networks for Semantic Segmentation
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
• Multi-Scale Context Aggregation by Dilated Convolutions
• Assessment of algorithms for mitosis detection in breast cancer histopathology images
• Gland Instance Segmentation Using Deep Multichannel Neural Networks
• Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues
• DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
• Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation

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