Deep multilevel contextual networks for biomedical image segmentation

Chen, Hao; Dou, Qi; Qi, Xiaojuan; Cheng, Jie-Zhi; Heng, Pheng‐Ann

doi:10.1016/b978-0-12-816176-0.00015-6

Cited by 38 publications

(66 citation statements)

References 26 publications

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“…Biomedical image segmentation plays a crucial role in many applications, such as population analysis, disease progression modelling, or treatment planning. Convolutional neural networks (CNNs), a class of deep learning methods, have been employed to derive powerful biomedical segmentation algorithms, showing promise of overcoming limitations in previous methods [3,4,29,34]. However, CNNbased approaches most often depend on (large-scale) training data, particularly in the form of image scans paired with segmentations.…”

Section: Introductionmentioning

confidence: 99%

Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

Dalca

Guttag

Sabuncu

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

139

102

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We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data that contains images and their manual segmentations. Instead, we employ unpaired segmentation images to build an anatomical prior. Critically these segmentations can be derived from imaging data from a different dataset and imaging modality than the current task. We introduce a generative probabilistic model that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervised setting. We conducted an empirical analysis of the proposed approach in the context of structural brain MRI segmentation, using a multi-study dataset of more than 14,000 scans. Our results show that an anatomical prior can enable fast unsupervised segmentation which is typically not possible using standard convolutional networks. The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable. The code is freely available at

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Section: Introductionmentioning

confidence: 99%

Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

Dalca

Guttag

Sabuncu

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

139

102

View full text Add to dashboard Cite

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“…This dataset [9] contains 85 training images (37 benign (BN), 48 malignant (MT)), 60 testing images (33 BN, 27 MT) in part A, and 20 testing images (4 BN, 16 MT) in part B. We modify the original CUMedNet [3] to make it deeper with two more encoding and decoding blocks (denoted as CUMedNet + ). We run all the experiments for the K-to-1 network and ablation study 5 times.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Due to these correlations, learning representation and feature transform for one object class can often indicate the existence of some other object classes (possibly nearby). A conventional way of using multi-class annotation maps is to treat a full annotation map as a whole subject and use spatial cross-entropy loss function to compare it with the model's outputs in back propagation [8,3,13]. Due to spatial correlations among different object classes, directly using annotations of all object classes to train a deep network may cause a deep network not to be able to fully exploring its representation learning ability for every object class, especially for those classes with small sizes and unclear/confusing appearance.…”

Section: Introductionmentioning

confidence: 99%

Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images

Zhang

Ying

Chen

2019

Lecture Notes in Computer Science

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Segmentation maps of medical images annotated by medical experts contain rich spatial information. In this paper, we propose to decompose annotation maps to learn disentangled and richer feature transforms for segmentation problems in medical images. Our new scheme consists of two main stages: decompose and integrate. Decompose: by annotation map decomposition, the original segmentation problem is decomposed into multiple segmentation sub-problems; these new segmentation sub-problems are modeled by training multiple deep learning modules, each with its own set of feature transforms. Integrate: a procedure summarizes the solutions of the modules in the previous stage; a final solution is then formed for the original segmentation problem. Multiple ways of annotation map decomposition are presented and a new end-to-end trainable K-to-1 deep network framework is developed for implementing our proposed "decompose-and-integrate" learning scheme. In experiments, we demonstrate that our decompose-and-integrate segmentation scheme, utilizing state-of-the-art fully convolutional networks (e.g., DenseVoxNet in 3D and CUMedNet in 2D), improves segmentation performance on multiple 3D and 2D datasets. Ablation study confirms the effectiveness of our proposed learning scheme for medical images.

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“…Most of the deep learning methods used for EM image analysis utilise the network topologies proposed previously on ImageNet challenge [12] as an encoder [13][14][15]. A decoder is subsequently used to retrieve the original resolution.…”

Section: Introductionmentioning

confidence: 99%

EM-net: Deep learning for electron microscopy image segmentation

Khadangi

Boudier

Rajagopal

2020

Preprint

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Recent high-throughput electron microscopy techniques such as focused ion-beam scanning electron microscopy (FIB-SEM) provide thousands of serial sections which assist the biologists in studying sub-cellular structures at high resolution and large volume. Low contrast of such images hinder image segmentation and 3D visualisation of these datasets. With recent advances in computer vision and deep learning, such datasets can be segmented and reconstructed in 3D with greater ease and speed than with previous approaches. However, these methods still rely on thousands of ground-truth samples for training and electron microscopy datasets require significant amounts of time for carefully curated manual annotations. We address these bottlenecks with EMnet, a scalable deep convolutional neural network for EM image segmentation. We have evaluated EM-net using two datasets, one of which belongs to an ongoing competition on EM stack segmentation since 2012. We show that EM-net variants achieve better performances than current deep learning methods using small-and medium-sized ground-truth datasets. We also show that the ensemble of top EM-net base classifiers outperforms other methods across a wide variety of evaluation metrics.

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Deep multilevel contextual networks for biomedical image segmentation

Cited by 38 publications

References 26 publications

Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images

EM-net: Deep learning for electron microscopy image segmentation

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