pwc-archive/papers-with-abstracts huggingface dataset:
Training deep segmentation networks on texture-encoded input: application to neuroimaging of the developing neonatal brain
Authors: Ahmed E. Fetit, John Cupitt, Turkay Kart, Daniel Rueckert
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Segmentation, Semantic Segmentation

Standard practice for using convolutional neural networks (CNNs) in semantic segmentation tasks assumes that the image intensities are directly used for training and inference. In natural images this is performed using RGB pixel intensities, whereas in medical imaging, e.g. magnetic resonance imaging (MRI), gray level pixel intensities are typically used. In this work, we explore the idea of encoding the image data as local binary textural maps prior to the feeding them to CNNs, and show that accurate segmentation models can be developed using such maps alone, without learning any representations from the images themselves. This questions common consensus that CNNs recognize objects from images by learning increasingly complex representations of shape, and suggests a more important role to image texture, in line with recent findings on natural images. We illustrate this for the first time on neuroimaging data of the developing neonatal brain in a tissue segmentation task, by analyzing large, publicly available T2-weighted MRI scans (n=558, range of postmenstrual ages at scan: 24.3 - 42.2 weeks) obtained retrospectively from the Developing Human Connectome Project cohort. Rapid changes in visual characteristics that take place during early brain development make it important to establish a clear understanding of the role of visual texture when training CNN models on neuroimaging data of the neonatal brain; this yet remains a largely understudied but important area of research. From a deep learning perspective, the results suggest that CNNs could simply be capable of learning representations from structured spatial information, and may not necessarily require conventional images as input.
A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling
Authors: Ahmed E. Fetit, Amir Alansary, Lucilio Cordero-Grande, John Cupitt, Alice B. Davidson, A. David Edwards, Joseph V. Hajnal, Emer Hughes, Konstantinos Kamnitsas, Vanessa Kyriakopoulou, Antonios Makropoulos, Prachi A. Patkee, Anthony N. Price, Mary A. Rutherford, Daniel Rueckert
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Image Segmentation, Semantic Segmentation

We developed an automated system based on deep neural networks for fast and sensitive 3D image segmentation of cortical gray matter from fetal brain MRI. The lack of extensive/publicly available annotations presented a key challenge, as large amounts of labeled data are typically required for training sensitive models with deep learning. To address this, we: (i) generated preliminary tissue labels using the Draw-EM algorithm, which uses Expectation-Maximization and was originally designed for tissue segmentation in the neonatal domain; and (ii) employed a human-in-the-loop approach, whereby an expert fetal imaging annotator assessed and refined the performance of the model. By using a hybrid approach that combined automatically generated labels with manual refinements by an expert, we amplified the utility of ground truth annotations while immensely reducing their cost (283 slices). The deep learning system was developed, refined, and validated on 249 3D T2-weighted scans obtained from the Developing Human Connectome Project's fetal cohort, acquired at 3T. Analysis of the system showed that it is invariant to gestational age at scan, as it generalized well to a wide age range (21 – 38 weeks) despite variations in cortical morphology and intensity  across the fetal distribution. It was also found to be invariant to intensities in regions surrounding the brain (amniotic fluid), which often present a major obstacle to the processing of neuroimaging data in the fetal domain.
4D Deep Learning for Multiple-Sclerosis Lesion Activity Segmentation
Authors: Nils Gessert, Marcel Bengs, Julia Krüger, Roland Opfer, Ann-Christin Ostwaldt, Praveena Manogaran, Sven Schippling, Alexander Schlaefer
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Decoder, Deep Learning, Lesion Segmentation, Segmentation

Multiple sclerosis lesion activity segmentation is the task of detecting new and enlarging lesions that appeared between a baseline and a follow-up brain MRI scan. While deep learning methods for single-scan lesion segmentation are common, deep learning approaches for lesion activity have only been proposed recently. Here, a two-path architecture processes two 3D MRI volumes from two time points. In this work, we investigate whether extending this problem to full 4D deep learning using a history of MRI volumes and thus an extended baseline can improve performance. For this purpose, we design a recurrent multi-encoder-decoder architecture for processing 4D data. We find that adding more temporal information is beneficial and our proposed architecture outperforms previous approaches with a lesion-wise true positive rate of 0.84 at a lesion-wise false positive rate of 0.19.
Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Mover’s discrepancies
Authors: Mattias P Heinrich, Lasse Hansen
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Domain Adaptation, Image Registration, Unsupervised Domain Adaptation

Multimodal image registration is a very challenging problem for deep learning approaches. Most current work focuses on either supervised learning that requires labelled training scans and may yield models that bias towards annotated structures or unsupervised approaches that are based on hand-crafted similarity metrics and may therefore not outperform their classical non-trained counterparts. We believe that unsupervised domain adaptation can be beneficial in overcoming the current limitations for multimodal registration, where good metrics are hard to define.
Domain adaptation has so far been mainly limited to classification problems. We propose the first use of unsupervised domain adaptation for discrete multimodal registration. Based on a source domain for which quantised displacement labels are available as supervision, we transfer the output distribution of the network to better resemble the target domain (other modality) using classifier discrepancies. To improve upon the sliced Wasserstein metric for 2D histograms, we present a novel approximation that projects predictions into 1D and computes the L1 distance of their cumulative sums. Our proof-of-concept demonstrates the applicability of domain transfer from mono- to multimodal (multi-contrast) 2D registration of canine MRI scans and improves the registration accuracy from 33% (using sliced Wasserstein) to 44%.
SAU-Net: Efficient 3D Spine MRI Segmentation Using Inter-Slice Attention
Authors: Yichi Zhang, Lin Yuan, Yujia Wang, Jicong Zhang
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: MRI segmentation, Segmentation

Accurate segmentation of spine Magnetic Resonance Imaging (MRI) is highly demanded in morphological research, quantitative analysis, and diseases identification, such as spinal canal stenosis, disc herniation and degeneration. However, accurate spine segmentation is challenging because of the irregular shape, artifacts and large variability between slices. To alleviate these problems, spatial information is used for more continuous and accurate segmentation such as by 3D convolutional neural networks (CNN) . However, 3D CNN suffers from higher computational cost, memory cost and risk of over-fitting, especially for medical images where the number of labeled data is limited. To address these problems, we apply the attention mechanism for the utilization of inter-slice information in 3D segmentation tasks based on 2D convolutional networks and propose a spatial attention-based densely connected U-Net (SAU-Net), which consists of Dense U-Net for extraction of intra-slice features and an inter-slice attention module (ISA) to utilize inter-slice information from adjacent slices and refine the segmentation results. Experimental results demonstrate the effectiveness of ISA as well as higher accuracy and efficiency of segmentation results of our method compared with other deep learning methods.
Mutual information deep regularization for semi-supervised segmentation
Authors: Jizong Peng, Marco Pedersoli, Christian Desrosiers
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

The scarcity of labeled data often limits the application of deep learning to medical image segmentation. Semi-supervised learning helps overcome this limitation by leveraging unlabeled images to guide the learning process. In this paper, we propose using a clustering loss based on mutual information that explicitly enforces prediction consistency between nearby pixels in unlabeled images, and for random perturbation of these images, while imposing the network to predict the correct labels for annotated images. Since mutual information does not require a strict ordering of clusters in two different cluster assignments, we propose to incorporate another consistency regularization loss which forces the alignment of class probabilities at each pixel of perturbed unlabeled images. We evaluate the method on three challenging publicly-available medical datasets for image segmentation. Experimental results show our method to outperform recently-proposed approaches for semi-supervised and yield a performance comparable to fully-supervised training.
Uncertainty-Aware Training of Neural Networks for Selective Medical Image Segmentation
Authors: Yukun Ding, Jinglan Liu, Xiaowei Xu, Meiping Huang, Jian Zhuang, JinJun Xiong, Yiyu Shi
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

State-of-the-art deep learning based methods have achieved remarkable performance on medical image segmentation. Their applications in the clinical setting are, however, limited due to the lack of trustworthiness and reliability. Selective image segmentation has been proposed to address this issue by letting a DNN model process instances with high confidence while referring difficult ones with high uncertainty to experienced radiologists. As such, the model performance is only affected by the predictions on the high confidence subset rather than the whole dataset. Existing selective segmentation methods, however, ignore this unique property of selective segmentation and train their DNN models by optimizing accuracy on the entire dataset. Motivated by such a discrepancy, we present a novel method in this paper that considers such uncertainty in the training process to maximize the accuracy on the confident subset rather than the accuracy on the whole dataset. Experimental results using the whole heart and great vessel segmentation and gland segmentation show that such a training scheme can significantly improve the performance of selective segmentation.
Domain adaptation model for retinopathy detection from cross-domain OCT images
Authors: Jing Wang, YiWei Chen, Wanyue Li, Wen Kong, Yi He, Chunhui Jiang, Guohua Shi
Date: 2020-01-29 00:00:00
Proceeding: MIDL 2019 7
Tasks: Domain Adaptation, Generative Adversarial Network

A deep neural network (DNN) can assist in retinopathy screening by automatically classifying patients into normal and abnormal categories according to optical coherence tomography (OCT) images. Typically, OCT images captured from different devices show heterogeneous appearances because of different scan settings; thus, the DNN model trained from one domain may fail if applied directly to a new domain. As data labels are difficult to acquire, we proposed a generative adversarial network-based domain adaptation model to address the cross-domain OCT images classification task, which can extract invariant and discriminative characteristics shared by different domains without incurring additional labeling cost. A feature generator, a Wasserstein distance estimator, a domain discriminator, and a classifier were included in the model to enforce the extraction of domain invariant representations. We applied the model to OCT images as well as public digit images. Results show that the model can significantly improve the classification accuracy of cross-domain images.
Well-Calibrated Regression Uncertainty in Medical Imaging with Deep Learning
Authors: Max-Heinrich Laves, Sontje Ihler, Jacob F. Fast, Lüder A. Kahrs, Tobias Ortmaier
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Bayesian Inference, Deep Learning, regression

The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance.
We apply estimation of predictive uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show why predictive uncertainty is systematically underestimated.
We suggest using $ \sigma $ scaling with a single scalar value; a simple, yet effective calibration method for both aleatoric and epistemic uncertainty.
The performance of our approach is evaluated on a variety of common medical regression data sets using different state-of-the-art convolutional network architectures.
In all experiments, $ \sigma $ scaling is able to reliably recalibrate predictive uncertainty.
It is easy to implement and maintains the accuracy.
Well-calibrated uncertainty in regression allows robust rejection of unreliable predictions or detection of out-of-distribution samples.
Skull-RCNN: A CNN-based network for the skull fracture detection
Authors: Zhuo Kuang, Xianbo Deng, Li Yu, Hang Zhang, Xian lin, Hui Ma
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Fracture detection, Region Proposal

Skull fractures, following head trauma, may bring several complications and cause epidural hematomas. Therefore, it is of great significance to locate the fracture in time. However, the manual detection is time-consuming and laborious, and the previous studies for the automatic detection could not achieve the accuracy and robustness for clinical application. In this work, based on the Faster R-CNN, we propose a novel method for more accurate skull fracture detection results, and we name it as the Skull R-CNN. Guiding by the morphological features of the skull, a skeleton-based region proposal method is proposed to make candidate boxes more concentrated in key regions and reduce invalid boxes. With this advantage, the region proposal network in Faster R-CNN is removed for less computation. On the other hand, a novel full resolution feature network is constructed to obtain more precise features to make the model more snesetive to small objects. Experiment results showed that most of skull fractures could be detected correctly by the proposed method in a short time. Compared to the previous works on the skull fracture detection, Skull R-CNN significantly reduces the false positives, and keeps a high sensitivity.
Correlation via Synthesis: End-to-end Image Generation and Radiogenomic Learning Based on Generative Adversarial Network
Authors: Ziyue Xu, Xiaosong Wang, Hoo-chang Shin, Dong Yang, Holger Roth, Fausto Milletari, Ling Zhang, Daguang Xu
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Generative Adversarial Network, Image Generation

Radiogenomic map linking image features and gene expression profiles has great potential for  non-invasively identifying molecular properties of a particular type of disease.  Conventionally, such map is produced in three independent steps: 1) gene-clustering to metagenes, 2) image feature extraction, and 3) statistical correlation between metagenes and image features. Each step is separately performed and relies on arbitrary measurements without considering the correlation among each other. In this work, we investigate the potential of an end-to-end method fusing gene code with image features to generate synthetic pathology image and learn radiogenomic map simultaneously. To achieve this goal, we develop a multi-conditional generative adversarial network (GAN) conditioned on both background images and gene expression code, synthesizing the corresponding image. Image and gene features are fused at different scales to ensure both the separation of pathology part and background, as well as the realism and quality of the synthesized image. We tested our method on non-small cell lung cancer (NSCLC) dataset. Results demonstrate that the proposed method produces realistic synthetic images, and provides a promising way to find gene-image relationship in a holistic end-to-end manner.
Siamese Tracking of Cell Behaviour Patterns
Authors: Andreas Panteli, Deepak K. Gupta, Nathan de Bruin, Efstratios Gavves
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Cell Tracking, Segmentation

Tracking and segmentation of biological cells in video sequences is a challenging problem, especially due to the similarity of the cells and high levels of inherent noise. Most machine learning-based approaches lack robustness and suffer from sensitivity to less prominent events such as mitosis, apoptosis and cell collisions. Due to the large variance in medical image characteristics, most approaches are dataset-specific and do not generalise well on other datasets. 

In this paper, we propose a simple end-to-end cascade neural architecture able to model the movement behaviour of biological cells and predict collision and mitosis events. Our approach uses U-Net for an initial segmentation which is then improved through processing by a siamese tracker capable of matching each cell along the temporal axis. By facilitating the re-segmentation of collided and mitotic cells, our method demonstrates its capability to handle volatile trajectories and unpredictable cell locations while being invariant to cell morphology. We demonstrate that our tracking approach achieves state-of-the-art results on  PhC-C2DL-PSC and Fluo-N2DH-SIM+ datasets and ranks second on the DIC-C2DH-HeLa dataset of the cell tracking challenge benchmarks.
Exploring Bayesian Deep Learning Uncertainty Measures for Segmentation of New Lesions in Longitudinal MRIs
Authors: Nazanin Mohammadi Sepahvand, Raghav Mehta, Douglas Lorne Arnold, Doina Precup, Tal Arbel
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7

In this paper, we develop a modified U-Net architecture to accurately segment new and
enlarging lesions in longitudinal MRI, based on multi-modal MRI inputs, as well as subtrac-
tion images between timepoints, in the context of large-scale clinical trial data for patients
with Multiple Sclerosis (MS). We explore whether MC-Dropout measures of uncertainty
lead to confident assertions when the network output is correct, and are uncertain when
incorrect, thereby permitting their integration into clinical workflows and downstream in-
ference tasks.
Iterative reconstruction artefact removal using null-space networks
Authors: Casper O. da Costa-Luis, Andrew J. Reader
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7

Incorporation of resolution modelling (RM) into iterative reconstruction produces Gibbs ringing artefacts which adversely affect clinically used metrics such as SUV${}_{\mbox{max}}$. We propose the use of a null space network as a regulariser to compensate for these artefacts without introducing bias.
Multi-Task Deep Learning: Simultaneous Segmentation and Survival Analysis via Cox Proportional Hazards Regression
Authors: JM Moreira, J Carvalho, N Papanikolaou, M Figueiredo
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Medical Image Analysis, Multi-Task Learning, regression, Segmentation, Survival Analysis

Multi-task learning has taken an important place as a tool for medical image analysis, namely for the development of predictive models of disease. This study aims at developing a new deep learning model for simultaneous segmentation and survival regression, using a version of the Cox model to support the learning process. We use a combination of a 2D U-net and a residual network to minimize a combined loss function for segmentation and survival regression. To validate our method, we created a simple synthetic data set - the model segments circles of different sizes and regresses the area of circles. The main motivation of this work is to create a workflow for segmentation and regression for medical images application: in specific, we use this model to segment lesions or organs and regress clinical outcomes as overall or disease-free survival.
Training Models 20X Faster in Medical Image Analysis
Authors: Dong Yang, Holger Roth, Xiaosong Wang, Ziyue Xu, Yan Cheng, Daguang Xu
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Medical Image Analysis

Analyzing high-dimensional medical images (2D/3D/4D CT, MRI, histopathological images, etc.) plays an important role in many biomedical applications, such as anatomical pattern understanding, disease diagnosis, and treatment planning. The AI assisted models have been widely adopted in the domain of medical image analysis with great successes. However, training such models with large-size data is expensive in terms of computation and memory consumption. In this work, we provide solutions for improving model training efficiency, which will speed up the training of AI models (20X faster on an exemplary 3D segmentation framework), and enable researchers and radiologists to improve the efficiency in their clinical studies. The overall efficiency improvement comes from both improved algorithms and engineering advance.
A Keypoint-based Morphological Signature for Large-scale Neuroimage Analysis
Authors: Laurent Chauvin, Matthew Toews
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Image Retrieval, Retrieval

We present an image keypoint-based morphological signature that can be used to efficiently assess the pair-wise whole-brain similarity for large MRI datasets. Similarity is assessed via Jaccard-like measure of set overlap based on the proportion of keypoints shared by an image pair, which may be evaluated in O(N log N) computational complexity given a set of N images using fast nearest neighbor indexing. Image retrieval experiments combine four large public neuroimage datasets including the Human Connectome Project (HCP), the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS), for a total of N=7536 T1-weighted MRIs of 3334 unique subjects. Our method identifies all pairs of same-subjects images based on a simple threshold, and revealed a number of previously unknown subject labeling errors.
Morphological Signature for Improvement of Weakly Supervised Segmentation of Quadriceps Muscles on Magnetic Resonance Imaging Data
Authors: Hoai-Thu Nguyen, Sylvain Grange, Magalie Viallon, Rémi Grange, Pierre Croisille, Olivier Bernard, Thomas Grenier
Date: 2020-01-25 00:00:00
Proceeding: MIDL 2019 7
Tasks: Data Augmentation, Medical Diagnosis, Segmentation, Weakly supervised segmentation

Automatic segmentation allows advancement in medical diagnosis and follow-up but remains a challenging task. Thanks to new machine learning approaches, this task tends to be more and more robust but still required many manual segmentations. Here we proposed to improve segmentation results obtained by multi-atlas segmentation with corrective learning (CL) approach using a selection of atlases based on morphological similarity to the image to process. We first introduce our morphological measurement dedicated for quadriceps segmentation of 3D T1 Water-only MR images and then use it to select closest atlases. Our results show that using few atlases (3 in lieu of 6) based on our morphological measurement improves segmentation quality and decrease computational time for multi-atlas segmentation with CL. Based on the measurements, we also defined a data augmentation strategy to train U-Net (a well-known and efficient deep learning segmentation approach), expecting better generalization capability, with very promising results.