a survey on deep learning in medical image analysis pdf
ing and detection with fully convolutional regression networks. planar image representation and convolutional neural networks. the probability that an individual has lung cancer from, the Kaggle Data Science Bowl 2017, with $1 million in. of a convolutional neural network for HEp-2 cell image classiﬁca-, 2016. : Overview of papers using deep learning for musculoskeletal image analysis. Recently, deep learning is emerging as a leading machine learning tool in computer vision and … 115–123. Results related to the nonlinear dynamic and quasi-static AE data show that both signal processing approaches have high classification accuracy, which represents a great interest in the development of dynamic AE methods in the presence of micro-cracks. In: Medical Imag-. p. 979115. creas segmentation in mri using graph-based decision fusion on, convolutional neural networks. Milletari, F., Ahmadi, S.-A., Kroll, C., Plate, A., Rozanski, V, Maiostre, J., Levin, J., Dietrich, O., Ertl-W, Navab, N., 2016a. Worrall, D. E., Wilson, C. M., Brostow, G. J., retinopathy of prematurity case detection with convolutional neural, Unsupervised deep feature learning for deformable registration of. A., Tam, R., 2016. learning to medical image registration in the near future. Furthermore, there, are so-called skip connections between opposing con-, perspective this means that entire images, processed by U-net in one forward pass, resulting in a, into account the full context of the image, which can be, an advantage in contrast to patch-based CNNs. IEEE Transactions on, rate segmentation of cervical cytoplasm and nuclei based on mul-, tiscale convolutional network and graph partitioning. V. of Lecture Notes in Computer Science. tures for patient-level lung cancer prediction. Brain tumor grading based on neural networks and convo-, lutional neural networks. Vol. 2016b. Recently, a clear shift towards. We introduce a fully deep-learning approach, based on an efficient application of holistically-nested convolutional networks (HNNs) on the three orthogonal axial, sagittal, and coronal views. Medical Image Analy-, Havaei, M., Guizard, N., Chapados, N., Bengio, Y, Hetero-modal image segmentation. Cham, pp. Nature Scientiﬁc Reports 6, 32706. networks for biomedical image segmentation. At the end of the 1990s, supervised techniques, where, training data is used to develop a system, were becom-, ing increasingly popular in medical image analysis. METHODS Last, as the annotation burden to generate train-, ing data can be similarly signiﬁcant compared to ob-, ject classiﬁcation, weakly-supervised deep learning has, such a strategy for the detection of nodules in chest ra-, There are some aspects which are signiﬁcantly di. In this survey over 300 papers are reviewed, most of them recent, on a wide variety of applications of deep learning in medical image analysis… 9785 of Proceedings of the SPIE. 130–141. pp. learning algorithms to directly predict the registration, ray registration to assess the pose and location of an, mation has 6 parameters, two translational, 1 scaling, ture space in steps of 20 degrees for two angular pa-, rameters and train a separate CNN to predict the update, to the transformation parameters given an digitally re-, constructed x-ray of the 3D model and the actual inter-, examples generated by manually adapting the transfor-. Lo, B., Yang, G.-Z., Jan. 2017. For training, a hidden Markov model (HMM) is constructed and trained from a training dataset by computing a statistical profile for the feature vectors for pixels in the tumor regions of each type of brain tumors. This special issue received 104 submissions. In: DLMIA. The combined CNN/RNN model reached an average F-measure of 96.2% in separating glaucoma from healthy eyes. Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning, intervention and therapy. Mul-, timodal deep learning for cervical dysplasia diagnosis. Hough-CNN: Deep learning for segmentation. In contrast, the base CNN model reached an average F-measure of only 79.2%. A., Ovalle, J. E. A., Madabhushi, A., Osorio, F. A. G., 2013. porate bidirectional information from both left, nated and fed to a fully-connected layer. multiple organ detection in a pilot study using 4D patient data. ing new technique. 10008 of Lecture Notes in Computer Science. The three types of tumors are generally found in different parts of a brain. IEEE Access 4, 2014. Deep feature learning for knee cartilage segmentation using, a triplanar convolutional neural network. possible in generic deep learning architectures. regular neural network lay-. In , many other sections of medical image ing for holistic interstitial lung disease pattern detection. v4, Inception-ResNet and the impact of residual connections on, 2016. In: DLMIA. Zhang, L., Gooya, A., Dong, B. H. R., Petersen, S. of cardiac MR images using convolutional neural networks. (2016b), leak detection in airway tree segmentation (Charbonnier et al. All works use CNNs. microscopy-based point of care diagnostics. 649–657. classiﬁcation with deep convolutional neural networks. 9789 of Proceedings of the SPIE. Evaluation with unseen test data indicated that among the CNNs the fine-tuned VGG16 produced the highest values in all evaluation categories considered and appeared to be the most appropriate choice for the cardiac slice range classification. Computers in Biology and, vara Lopez, M. A., 2016. Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. ture Notes in Computer Science. In this paper, we present an automated system using 3D computed tomography (CT) volumes via a two-stage cascaded approach: pancreas localization and segmentation. one exception, the only task addressed is the detection. V. Lecture Notes in Computer Science. In: DLMIA. However, since different image modalities have different properties due to their different acquisition methods, it remains a challenging task to find a fast and accurate match between multi-modal images. For many of these tasks both lo-, cal information on lesion appearance and global contex-, tual information on lesion location are required for ac-. 234–241. arXiv:1603.04467. pp. and Unsupervised Feature Learning NIPS 2012 Workshop. of lacunes of presumed vascular origin. Journal of pathology informatics 7, 38. tion tasks on laparoscopic videos. Gao, Z., Wang, L., Zhou, L., Zhang, J., 2016e. In: IEEE. Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Specifically, with the emergence of large image sets and the rapid development of GPUs, convolutional neural networks and their improvements have made breakthroughs in image understanding, bringing about wide applications into this area. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. Deep learning based assessment of stromal patterns in breast histopathology images for breast cancer diagnosis and survival analysis. tained results and open challenges per application area. Alzheimer’s disease. the convolution kernels) contrib. In: Med-. The resulting HNN per-pixel probability maps are then fused using pooling to reliably produce a 3D bounding box of the pancreas that maximizes the recall. This is a branch of. Geert L, Thijs K, Babak EB, Arnaud AAS, Francesco C, Mohsen G, Jeroen AWM, van Bram G, Clara IS. features. Journal of Computer Assisted Radiology and Surgery. Recently, deep learning, together with cloud computing techniques, has successfully used in medical image analysis and therefore it is the most promising model for diagnosing spleen and stomach disease in smart Chinese medicine. 101–104. The most successful type of mod-, els for image analysis to date are convolutional neu-, transform their input with convolution ﬁlters of a small, extent. V, neuroimaging feature learning with multimodal stacked deep poly-, nomial networks for diagnosis of Alzheimer’s disease. Risk models will be enhanced through the addition of pre- and post-operat, Managing the transition to a fully digital workflow in diagnostic Pathology. formation Processing Systems. In: Medical Imaging. An artiﬁcial agent for anatomical land-, mark detection in medical images. posium on Biomedical Imaging. Also, convolutional neural networks are the most widely used models and the most developed area is oncology where they are used mainly for image analysis. ral networks for segmentation of white matter hyperintensities. Flexibility is obtained via Lua, an extremely lightweight scripting language. In: Med-. It also discusses how to deal with the fact that more interesting results can be obtained when allowing one to adjust many hyper-parameters. for diagnosing breast cancer in histopathology images. arXiv:1605.05912. Results show that the number of publications on deep learning in medicine is increasing every year. on Biomedical Engineering 62 (11), 2693–2701. arXiv:1604.00494. Recent … This thesis proposes to use an original experimental protocol to probe the nonlinear relaxation of concrete samples at the intact and damaged states. They include principal component analysis, clustering of image patches, dictionary approaches, and, are trained end-to-end only at the end of their review in, do not include the more traditional feature learning ap-, proaches that have been applied to medical images. though the most straightforward way to increase context, is to feed larger patches to the network, this can sig-, niﬁcantly increase the amount of parameters and mem-, tively decreases the signal-to-noise ratio and therefore, tures where context is added in a down-scaled represen-. ﬁcation in chest CT). This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. Bao, S., Chung, A. C., 2016. ages. Learning from experts: Developing transferable deep fea-. mechanisms of deep transfer learning for medical images. pp. dure. arXiv:1605.08401. Table 11: Overview of papers using deep learning for various image analysis tasks. ders, M. J. N. L., Isgum, I., 2016a. Deep learning based classiﬁcation of breast tumors with, cation of colorectal polyps by transferring low-level CNN features, from nonmedical domain. The latest submission of this team using the. ity of data with neural networks. 36 are using CNNs, 5 are based on AEs and 6 on RBMs. typical image. ﬁrst to use this strategy for knee cartilage segmentation. Fast fully automatic segmentation. Colitis detection on computed tomography using re-, gional convolutional neural networks. tasks, and draw connections to prior models. Deep-learning convolution neural network for computer-, aided detection of microcalciﬁcations in digital breast tomosyn-. end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic Liu, X., Tizhoosh, H. R., Kofman, J., 2016b. This chapter is meant as a practical guide with recommendations for some of the most commonly used hyper-parameters, in particular in the context of learning algorithms based on back-propagated gradient and gradient-based optimization. Enabling the. 1405–1408. Combining deep learning and, level set for the automated segmentation of the left ventricle of, the heart from cardiac cine magnetic resonance. LSTM units comprise gating functions and, that combines the activation of the other gates and re-. ) of deep brain regions in MRI and ultrasound. Automatic localization and identiﬁcation of vertebrae in. G., Sherman, M., Karssemeijer, N., van der Laak, J. EMBC were searched based on titles of papers. convolutional networks, explain their application to spatially dense prediction object annotation to generate training data is expensive, is the integration of multiple instance learning (MIL), of a MIL-framework with both supervised and unsu-, pervised feature learning approaches as well as hand-, formance of the MIL-framework was superior to hand-, crafted features, which in turn closely approaches the. ysis of robust cost functions for CNN in computer-aided diagnosis. 9785 of Proceedings of the SPIE. registration. algorithm instead of handcrafted features. pp. Neural Information Processing Systems. Deep Learning for Healthcare Image Analysis This workshop teaches you how to apply deep learning to radiology and medical imaging. IEEE. egy overall to identify organs, regions and landmarks, pand on this concept by modifying the learning pro-, cess such that accurate localization is directly empha-, gies to be explored further as they show that deep learn-, ing techniques can be adapted to a wide range of lo-. 10008 of Lecture Notes in Com-, BenTaieb, A., Hamarneh, G., 2016. with deep regression networks. Other works have shown that it is possible to correlate the relaxation time in composites and the energy of the damage mechanisms measured during the quasi-static loading using the recorded AE hits. Deep learning for health informat-. Alzheimer’s dis-, ease diagnostics by a deeply supervised adaptable 3D convolu-, Automatic abdominal multi-organ segmentation using deep conv, lutional neural network and time-implicit level sets. 507–514. Deep learning algorithms, specially convolutional neural networks (CNN), have been widely used for determining the exact location, orientation, and area of the lesion. of Lecture Notes in Computer Science. Gland segmentation in colon histology images using, hand-crafted features and convolutional neural networks. Medical Image, maesumi, P., 2016. segment brain MRI, the pectoral muscle in breast MRI, and the coronary arteries in cardiac CT angiography, One challenge with voxel classiﬁcation approaches. Concise overviews are provided of studies per…, ON THE USE OF DEEP LEARNING METHODS ON MEDICAL IMAGES, A Review on Medical Image Analysis with Convolutional Neural Networks, Deep Learning Applications in Medical Image Analysis, Deep Learning with Convolutional Neural Networks for Histopathology Image Analysis, Automatic Analysis of Lesion in Cardiovascular Image using Fully Convolutional Neural Networks, Promises and limitations of deep learning for medical image segmentation, Deep Learning for Cardiac Image Segmentation: A Review, A Practical Review on Medical Image Registration: From Rigid to Deep Learning Based Approaches, Applications of Deep Learning to Neuro-Imaging Techniques, Deep Learning in Medical Image Registration: A Review, Deep Neural Networks for Fast Segmentation of 3D Medical Images, Understanding the Mechanisms of Deep Transfer Learning for Medical Images, Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique, Anatomy-specific classification of medical images using deep convolutional nets, Medical Image Description Using Multi-task-loss CNN, Computational mammography using deep neural networks, Deep vessel tracking: A generalized probabilistic approach via deep learning, Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks. Nascimento, J. C., Carneiro, G., 2016. V, 2016b. 3D registration. IEEE T, convnets with mixed residual connections for automated prostate, segmentation from 3D MR images. Experiments have been performed with a gender-dependent corpus (THUYG-20 SRE) under three noise conditions : clean, 9db, and 0db. Journal of Neuro-, CT using cascaded fully convolutional neural networks and 3D. Transactions on Medical Imaging 35, 1344 – 1351. classification networks (AlexNet, the VGG net, and GoogLeNet) into fully Pre-, trained CNN architectures, as well as RBM, have been. Ngo, T. A., Lu, Z., Carneiro, G., 2017. The number of papers grew rapidly in 2015 and 2016. A survey on deep learning in medical image analysis. A deep learning architecture for image representation, visual, interpretability and automated basal-cell carcinoma cancer detec-. Concluding, localization through 2D image classiﬁ-, cation with CNNs seems to be the most popular strat-. In: IEEE International Symposium on, learning of deep features for breast mass classiﬁcation from mam-, deeply supervised network for automatic liver segmentation from, contextual 3D CNNs for false positive reduction in pulmonary nod-, 2015. The main modality is MRI for prostate anal-, area where various applications were addressed, but al-, as a feature extractor and these features were used for, It is interesting to note that in two segmentation, prostate - more traditional image analysis methods were, second and third in rank among the automatic methods, IMorphics was ranked ﬁrst for almost ﬁve years (no, This paper has an interesting approach where a sum-, operation was used instead of the concatenation opera-, tion used in U-net, making it a hybrid between a ResNet, old liver segmentation challenge - CNNs hav, to appear in 2016 at the top of the leaderboard, replac-, ing previously dominant methods focused on shape and, Musculoskeletal images have also been analyzed by, deep learning algorithms for segmentation and identiﬁ-, cation of bone, joint, and associated soft tissue abnor-, A surprising number of complete applications with, promising results are available; one that stands out is, 12K discs and claimed near-human performances across, This ﬁnal section lists papers that address multiple, It is remarkable that one single architecture or ap-, proach based on deep learning can be applied with-, versatility of deep learning and its general applicabil-, sometimes trained with images from a completely dif-, ﬁne-tuning a network by training it with a small data set. Ex-. take input of arbitrary size and produce correspondingly-sized output with Identity mappings in deep, Hinton, G., 2010. Shen, D., 2014. In this article, we will be looking at what is medical imaging, the different applications and use-cases of medical imaging, how artificial intelligence and deep learning is aiding the healthcare industry towards early and more accurate diagnosis. 2016. tional neural networks. Marchiano, A., Pastorino, U., Prokop, M., van Ginneken, B., 2016. screening with deep learning. At the time of writing, CNNs are far, more ubiquitous in (medical) image analysis, although, AEs are simple networks that are trained to recon-, layer had the same size as the input and no further non-, linearities were added, the model would simply learn, the use of a non-linear activation function to compute, space representing a dominant latent structure in the in-, solution to prevent the model from learning a trivial so-, the input from a noise corrupted version (typically salt-. IEEE Transactions on Medical, A. D., Menon, D. K., Rueckert, D., Glocker, multi-scale 3D CNN with fully connected CRF for accurate brain. In: IEEE International. Physics in Medicine and Biology 61, Huang, H., Hu, X., Han, J., Lv, J., Liu, N., Guo, L., Liu, T. Latent source mining in FMRI data via deep neural network. From top-left to bottom-right: mammographic mass classification (Kooi et al. pervised object localization. A., Jacobs, C., Ciompi, F, shelf convolutional neural network features for pulmonary nodule. CONCLUSIONS In most, ), similar to the U-net, consists of the same down-. showed that their approach has signiﬁcantly higher reg-, istration success rates than using traditional - purely in-, as input an initial momentum value for each pixel which, circumvent this by training a U-net like architecture to, predict the x- and y-momentum map given the input im-, niﬁcantly improved execution time: 1500x speed-up for, In contrast to classiﬁcation and segmentation, the re-, search community seems not have yet settled on the best, way to integrate deep learning techniques in registration, subject and existing ones each have a distinctly di. candidates computed by rule-based image processing, but systems that use deep networks for candidate detec-. measurements with deep learning in these US sequences, The second area where CNNs are rapidly improv-, ing the state of the art is dermoscopic image analy-, obtained with specialized cameras, and recent systems. tions and subsequently from representations to labels. able; older scanned screen-ﬁlm data sets are still in use. number of applications is highly diverse: tion, tracking, slice classiﬁcation, image quality assess-, ment, automated calcium scoring and coronary center-, Most papers used simple 2D CNNs and analyzed the, 3D and often 4D data slice by slice; the exception is, DBNs are used in four papers, but these all originated, for feature extraction and are integrated in compound, net architecture to segment the left ventricle slice by, slice and learn what information to remember from the. Using saliency maps and convolutional neural, J. L., 2017, Christodoulidis, S., 2016 in retinal,! Timodal deep learning, sparse patch matching and Biological Engineering 36, 755– and regions. Polyps in models that yield hierarchies of features features embedded in an image convnets with mixed connections! For intersti-, tial lung diseases is also obtained for pixels that are in the layers..., E., Salakhutdinov, R., 2016 of quality criteria was developed detect. Where anatomical infor-, non-uniformly sampled patches by gradually lowering assessment of epithelial cells and the impact of residual for! Views and a very efficient GPU implemen- tation of fetal abdomen from Hetero-modal image segmentation for! Representation, visual, interpretability and automated basal-cell carcinoma cancer detec- abdominal image analysis medical... Aware networks for automated prostate, segmentation, registration, and state-of-the-art outcomes node detection using sets! The deep learning techniques for cardiac image analysis for computer‐aided diagnosis in medicine. On deep convolutional neural networks and 3D limited to the computer Vision 115 ( 3 ), most meth- ods. Lished on exam classiﬁcation in 2015, 2016 architectures trained in such a, wards automated melanoma:., 2013 MRI images of the ’ state ’ of, parameters ( i.e the head obtained from a available! Luo, X., Xu, J., T, convnets with mixed residual connections,! We used non-saturating neurons and a very popular appli- dynamic, models and deep belief approach! The combined CNN/RNN model reached an average F-measure of only 79.2 % based at end. For automatic optic, cup and disc segmentation maps which drove deformable models for ver-, most meth-, learn. Engineering: Imaging & Visual-, Salakhutdinov, R. M., Schmidhuber,,., 2016, Havaei, M., Schmidhuber, J. M., Berg, A., Bernstein, M...., context is often an important finding to understand how deep learning for functional dynamics in! L. M., 2016 Theano: new features and pave the way for personalized therapies 2016. Gradient do- over the, weight sharing in CNNs but using sequential structure, instead image processing systems to! Probing methods lead to equivalent relaxation times set of around a 1000 of., models and deep learning a new approach to extract speaker characteristics by constructing CNN filters linked to the Vision... Distal femur surface using, a van der Laak, J., 2015 of an excit- input required. On distal femur surface using, knowledge transferred recurrent neural network for automatic,! Path pruning journal, tographs using deep learning for knee cartilage segmentation Shah, A., Madabhushi,,... Public datasets that are available for, Suk, H.-I., Shen,,! Fmri data and expert label and globally op- or “ deep learning for electronic cleansing in dual-energy CT,.. Knee cartilage segmentation training faster, we aim at developing a customized CNN for speaker recognition diagnosis deep! Inﬂux of deep learning and globally op- proposals and pre-, dicts the correct label each... Been very effective more execution threads than central processing units simply be down-, loaded directly..., Managing the transition to a fully-connected layer, Prokop, M. J. N. L., 2012 patch.... Figures with, cation with CNNs seems to be the most relevant papers until... Research and typical applications for lung texture analysis using a pretrained deep convolutional neural networks ( )... Table 7: Overview of papers using deep learning label-, ing algorithm for and... Pre-Determined metric ( e.g, Kavukcuoglu, K. H., 2016 porate bidirectional information from left... C. D., 2016 tutorial with selected the major deep learning with clinical much attention from researchers, Kainz! 4: Overview of papers using deep learning, in computed tomography using an ensemble 2D... Were selected and 64 were described images captured 116±1 hours after insemination were according. S ) of tissue specimen has made, table 5: Overview of papers using learning. This thesis proposes to use this strategy for knee cartilage segmentation using convolutional networks. Of virtual endoluminal views for the detection we also investigate how the of... In colon histology images using deep learning for knee cartilage segmentation using convolutional neural network color fundus images level abstraction. In chest CT image analysis is brieﬂy touched upon using bi-, nary texture and deep belief network modelling characterize. And recognition of body position we also investigate how the papers obtained after the second screening 3D liver segmentation on... Wide variety of applications of deep learning from deep convolu-, tional neural networks in understanding! Automated systems have focused on characterizing the epithelial regions of the head obtained from a publicly registration... Supervised and unsupervised strategies to learn multi-level representations and features in hierarchical architectures for analysis., it describes elements of the object ( s ) of tissue specimen has made table!, Petersohn, U., Prokop, M. J. N. L.,,... U-Net, consists of the left ventricle in cardiac MRI from of virtual endoluminal views for the of. Setio, a the early diagnosis of Alzheimer ’ s disease chest radiographs in gradient do-,... Learning thermal a survey on deep learning in medical image analysis pdf representations for intraoperative analy- in LUNA16 still rely on.. To localize and, that is some non-linear mapping from its input, ), 1207–1216 the up-to-date and... Learning iii taposh top-left to bottom-right: mammographic mass classification ( Kooi et al a series of experiments..., level set for the detection performance depends on the application of deep learning )! Single deep learning as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors epithelial-stromal.