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Sha Q, Sun K, Jiang C, Xu M, Xue Z, Cao X, Shen D. Detail-preserving image warping by enforcing smooth image sampling. Neural Netw 2024; 178:106426. [PMID: 38878640 DOI: 10.1016/j.neunet.2024.106426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 04/14/2024] [Accepted: 06/01/2024] [Indexed: 08/13/2024]
Abstract
Multi-phase dynamic contrast-enhanced magnetic resonance imaging image registration makes a substantial contribution to medical image analysis. However, existing methods (e.g., VoxelMorph, CycleMorph) often encounter the problem of image information misalignment in deformable registration tasks, posing challenges to the practical application. To address this issue, we propose a novel smooth image sampling method to align full organic information to realize detail-preserving image warping. In this paper, we clarify that the phenomenon about image information mismatch is attributed to imbalanced sampling. Then, a sampling frequency map constructed by sampling frequency estimators is utilized to instruct smooth sampling by reducing the spatial gradient and discrepancy between all-ones matrix and sampling frequency map. In addition, our estimator determines the sampling frequency of a grid voxel in the moving image by aggregating the sum of interpolation weights from warped non-grid sampling points in its vicinity and vectorially constructs sampling frequency map through projection and scatteration. We evaluate the effectiveness of our approach through experiments on two in-house datasets. The results showcase that our method preserves nearly complete details with ideal registration accuracy compared with several state-of-the-art registration methods. Additionally, our method exhibits a statistically significant difference in the regularity of the registration field compared to other methods, at a significance level of p < 0.05. Our code will be released at https://github.com/QingRui-Sha/SFM.
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Affiliation(s)
- Qingrui Sha
- School of Biomedical Engineering, ShanghaiTech, Shanghai, China.
| | - Kaicong Sun
- School of Biomedical Engineering, ShanghaiTech, Shanghai, China.
| | - Caiwen Jiang
- School of Biomedical Engineering, ShanghaiTech, Shanghai, China.
| | - Mingze Xu
- School of Science and Engineering, Chinese University of Hong Kong-Shenzhen, Guangdong, China.
| | - Zhong Xue
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Xiaohuan Cao
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech, Shanghai, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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2
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Kuang S, Woodruff HC, Granzier R, van Nijnatten TJA, Lobbes MBI, Smidt ML, Lambin P, Mehrkanoon S. MSCDA: Multi-level semantic-guided contrast improves unsupervised domain adaptation for breast MRI segmentation in small datasets. Neural Netw 2023; 165:119-134. [PMID: 37285729 DOI: 10.1016/j.neunet.2023.05.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/09/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023]
Abstract
Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which arises from different vendors, acquisition protocols, and biological heterogeneity, remains an important but challenging obstacle on the path towards clinical implementation. In this paper, we propose a novel Multi-level Semantic-guided Contrastive Domain Adaptation (MSCDA) framework to address this issue in an unsupervised manner. Our approach incorporates self-training with contrastive learning to align feature representations between domains. In particular, we extend the contrastive loss by incorporating pixel-to-pixel, pixel-to-centroid, and centroid-to-centroid contrasts to better exploit the underlying semantic information of the image at different levels. To resolve the data imbalance problem, we utilize a category-wise cross-domain sampling strategy to sample anchors from target images and build a hybrid memory bank to store samples from source images. We have validated MSCDA with a challenging task of cross-domain breast MRI segmentation between datasets of healthy volunteers and invasive breast cancer patients. Extensive experiments show that MSCDA effectively improves the model's feature alignment capabilities between domains, outperforming state-of-the-art methods. Furthermore, the framework is shown to be label-efficient, achieving good performance with a smaller source dataset. The code is publicly available at https://github.com/ShengKuangCN/MSCDA.
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Affiliation(s)
- Sheng Kuang
- The D-Lab, Department of Precision Medicine, GROW - School or Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW - School or Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Renee Granzier
- Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Thiemo J A van Nijnatten
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; GROW - School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Marc B I Lobbes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; GROW - School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands; Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
| | - Marjolein L Smidt
- Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands; GROW - School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW - School or Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Siamak Mehrkanoon
- Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands.
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3
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Zhang C, Yang G, Li F, Wen Y, Yao Y, Shu H, Simon A, Dillenseger JL, Coatrieux JL. CTANet: Confidence-based Threshold Adaption Network for Semi-supervised Segmentation of Uterine Regions from MR Images for HIFU Treatment. Ing Rech Biomed 2023. [DOI: 10.1016/j.irbm.2022.100747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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4
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Krithika alias AnbuDevi M, Suganthi K. Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET. Diagnostics (Basel) 2022; 12:diagnostics12123064. [PMID: 36553071 PMCID: PMC9777361 DOI: 10.3390/diagnostics12123064] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
In biomedical image analysis, information about the location and appearance of tumors and lesions is indispensable to aid doctors in treating and identifying the severity of diseases. Therefore, it is essential to segment the tumors and lesions. MRI, CT, PET, ultrasound, and X-ray are the different imaging systems to obtain this information. The well-known semantic segmentation technique is used in medical image analysis to identify and label regions of images. The semantic segmentation aims to divide the images into regions with comparable characteristics, including intensity, homogeneity, and texture. UNET is the deep learning network that segments the critical features. However, UNETs basic architecture cannot accurately segment complex MRI images. This review introduces the modified and improved models of UNET suitable for increasing segmentation accuracy.
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Frueh M, Kuestner T, Nachbar M, Thorwarth D, Schilling A, Gatidis S. Self-supervised learning for automated anatomical tracking in medical image data with minimal human labeling effort. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107085. [PMID: 36044801 DOI: 10.1016/j.cmpb.2022.107085] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 08/02/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Tracking of anatomical structures in time-resolved medical image data plays an important role for various tasks such as volume change estimation or treatment planning. State-of-the-art deep learning techniques for automated tracking, while providing accurate results, require large amounts of human-labeled training data making their wide-spread use time- and resource-intensive. Our contribution in this work is the implementation and adaption of a self-supervised learning (SSL) framework that addresses this bottleneck of training data generation. METHODS To this end we adapted and implemented an SSL framework that allows for automated anatomical tracking without the necessity for human-labeled training data. We evaluated this method by comparison to conventional- and deep learning optical flow (OF)-based tracking methods. We applied all methods on three different time-resolved medical image datasets (abdominal MRI, cardiac MRI, and echocardiography) and assessed their accuracy regarding tracking of pre-defined anatomical structures within and across individuals. RESULTS We found that SSL-based tracking as well as OF-based methods provide accurate results for simple, rigid and smooth motion patterns. However, regarding more complex motion, e.g. non-rigid or discontinuous motion patterns in the cardiac region, and for cross-subject anatomical matching, SSL-based tracking showed markedly superior performance. CONCLUSION We conclude that automated tracking of anatomical structures on time-resolved medical image data with minimal human labeling effort is feasible using SSL and can provide superior results compared to conventional and deep learning OF-based methods.
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Affiliation(s)
- Marcel Frueh
- University Hospital Tuebingen, Department of Radiology, University of Tuebingen, Hoppe-Seyler-Straße 3 Tuebingen 72076, Germany; University of Tuebingen, Institute for Visual Computing, Department of Computer Science, Sand 14 Tuebingen 72076, Germany
| | - Thomas Kuestner
- University Hospital Tuebingen, Department of Radiology, University of Tuebingen, Hoppe-Seyler-Straße 3 Tuebingen 72076, Germany
| | - Marcel Nachbar
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tuebingen, Hoppe-Seyler-Straße 3 Tuebingen 72076, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tuebingen, Hoppe-Seyler-Straße 3 Tuebingen 72076, Germany
| | - Andreas Schilling
- University of Tuebingen, Institute for Visual Computing, Department of Computer Science, Sand 14 Tuebingen 72076, Germany
| | - Sergios Gatidis
- University Hospital Tuebingen, Department of Radiology, University of Tuebingen, Hoppe-Seyler-Straße 3 Tuebingen 72076, Germany; Max Planck Institute for Intelligent Systems, Empirical Inference Department, Max-Planck-Ring 4 Tuebingen 72076, Germany.
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6
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Huang L, Gao C, Liu N. DEFEAT: Decoupled feature attack across deep neural networks. Neural Netw 2022; 156:13-28. [DOI: 10.1016/j.neunet.2022.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/31/2022] [Accepted: 09/08/2022] [Indexed: 10/14/2022]
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7
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Amorosino G, Peruzzo D, Redaelli D, Olivetti E, Arrigoni F, Avesani P. DBB - A Distorted Brain Benchmark for Automatic Tissue Segmentation in Paediatric Patients. Neuroimage 2022; 260:119486. [PMID: 35843515 DOI: 10.1016/j.neuroimage.2022.119486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 06/30/2022] [Accepted: 07/13/2022] [Indexed: 10/17/2022] Open
Abstract
T1-weighted magnetic resonance images provide a comprehensive view of the morphology of the human brain at the macro scale. These images are usually the input of a segmentation process that aims detecting the anatomical structures labeling them according to a predefined set of target tissues. Automated methods for brain tissue segmentation rely on anatomical priors of the human brain structures. This is the reason why their performance is quite accurate on healthy individuals. Nevertheless model-based tools become less accurate in clinical practice, specifically in the cases of severe lesions or highly distorted cerebral anatomy. More recently there are empirical evidences that a data-driven approach can be more robust in presence of alterations of brain structures, even though the learning model is trained on healthy brains. Our contribution is a benchmark to support an open investigation on how the tissue segmentation of distorted brains can be improved by adopting a supervised learning approach. We formulate a precise definition of the task and propose an evaluation metric for a fair and quantitative comparison. The training sample is composed of almost one thousand healthy individuals. Data include both T1-weighted MR images and their labeling of brain tissues. The test sample is a collection of several tens of individuals with severe brain distortions. Data and code are openly published on BrainLife, an open science platform for reproducible neuroscience data analysis.
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Affiliation(s)
- Gabriele Amorosino
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy.
| | - Denis Peruzzo
- Neuroimaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | | | - Emanuele Olivetti
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy
| | - Filippo Arrigoni
- Paediatric Radiology and Neuroradiology Department, V. Buzzi Children's Hospital, Milan, Italy
| | - Paolo Avesani
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy
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8
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Landslide Susceptibility Prediction Based on High-Trust Non-Landslide Point Selection. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11070398] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Landslide susceptibility prediction has the disadvantages of being challenging to apply to expanding landslide samples and the low accuracy of a subjective random selection of non-landslide samples. Taking Fu’an City, Fujian Province, as an example, a model based on a semi-supervised framework using particle swarm optimization to optimize extreme learning machines (SS-PSO-ELM) is proposed. Based on the landslide samples, a semi-supervised learning framework is constructed through Density Peak Clustering (DPC), Frequency Ratio (FR), and Random Forest (RF) models to expand and divide the landslide sample data. The landslide susceptibility was predicted using high-trust sample data as the input variables of the data-driven model. The results show that the area under the curve (AUC) valued at the SS-PSO-ELM model for landslide susceptibility prediction is 0.893 and the root means square error (RMSE) is 0.370, which is better than ELM and PSO-ELM models without the semi-supervised framework. It shows that the SS-PSO-ELM model is more effective in landslide susceptibility. Thus, it provides a new research idea for predicting landslide susceptibility.
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9
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Fajar A, Sarno R, Fatichah C, Fahmi A. Reconstructing and resizing 3D images from DICOM files. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2020.12.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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10
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Henschel L, Kügler D, Reuter M. FastSurferVINN: Building resolution-independence into deep learning segmentation methods-A solution for HighRes brain MRI. Neuroimage 2022; 251:118933. [PMID: 35122967 PMCID: PMC9801435 DOI: 10.1016/j.neuroimage.2022.118933] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/22/2021] [Accepted: 01/24/2022] [Indexed: 01/04/2023] Open
Abstract
Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below 1.0 mm for improved structure definition and morphometry. Yet, only few, time-intensive automated image analysis pipelines have been validated for high-resolution (HiRes) settings. Efficient deep learning approaches, on the other hand, rarely support more than one fixed resolution (usually 1.0 mm). Furthermore, the lack of a standard submillimeter resolution as well as limited availability of diverse HiRes data with sufficient coverage of scanner, age, diseases, or genetic variance poses additional, unsolved challenges for training HiRes networks. Incorporating resolution-independence into deep learning-based segmentation, i.e., the ability to segment images at their native resolution across a range of different voxel sizes, promises to overcome these challenges, yet no such approach currently exists. We now fill this gap by introducing a Voxel-size Independent Neural Network (VINN) for resolution-independent segmentation tasks and present FastSurferVINN, which (i) establishes and implements resolution-independence for deep learning as the first method simultaneously supporting 0.7-1.0 mm whole brain segmentation, (ii) significantly outperforms state-of-the-art methods across resolutions, and (iii) mitigates the data imbalance problem present in HiRes datasets. Overall, internal resolution-independence mutually benefits both HiRes and 1.0 mm MRI segmentation. With our rigorously validated FastSurferVINN we distribute a rapid tool for morphometric neuroimage analysis. The VINN architecture, furthermore, represents an efficient resolution-independent segmentation method for wider application.
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Affiliation(s)
- Leonie Henschel
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - David Kügler
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Martin Reuter
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
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11
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Li S, Liu J, Song Z. Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net. INT J MACH LEARN CYB 2022; 13:2435-2445. [PMID: 35378734 PMCID: PMC8967694 DOI: 10.1007/s13042-022-01536-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 02/24/2022] [Indexed: 11/30/2022]
Abstract
Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-of-interest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue’s disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients’ data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.
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Affiliation(s)
- Shidong Li
- Department of Mathematics, San Francisco University, San Francisco, CA 94132 USA
| | - Jianwei Liu
- School of Mathematics, Tianjin University, Tianjin, 300354 China
- Tianjin Key Laboratory of Brain-Inspired Intelligence Technology, Tianjin, 300072 China
| | - Zhanjie Song
- School of Mathematics, Tianjin University, Tianjin, 300354 China
- Tianjin Key Laboratory of Brain-Inspired Intelligence Technology, Tianjin, 300072 China
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12
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Guo W, Wang Z, Ma M, Chen L, Yang H, Li D, Du W. Semi‐supervised multiple empirical kernel learning with pseudo empirical loss and similarity regularization. INT J INTELL SYST 2022. [DOI: 10.1002/int.22690] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Wei Guo
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education East China University of Science and Technology Shanghai People's Republic of China
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai People's Republic of China
| | - Zhe Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education East China University of Science and Technology Shanghai People's Republic of China
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai People's Republic of China
| | - Menghao Ma
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education East China University of Science and Technology Shanghai People's Republic of China
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai People's Republic of China
| | - Lilong Chen
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai People's Republic of China
| | - Hai Yang
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai People's Republic of China
| | - Dongdong Li
- Department of Computer Science and Engineering East China University of Science and Technology Shanghai People's Republic of China
| | - Wenli Du
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education East China University of Science and Technology Shanghai People's Republic of China
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13
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Intelligent Model for Brain Tumor Identification Using Deep Learning. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022. [DOI: 10.1155/2022/8104054] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Brain tumors can be a major cause of psychiatric complications such as depression and panic attacks. Quick and timely recognition of a brain tumor is more effective in tumor healing. The processing of medical images plays a crucial role in assisting humans in identifying different diseases. The classification of brain tumors is a significant part that depends on the expertise and knowledge of the physician. An intelligent system for detecting and classifying brain tumors is essential to help physicians. The novel feature of the study is the division of brain tumors into glioma, meningioma, and pituitary using a hierarchical deep learning method. The diagnosis and tumor classification are significant for the quick and productive cure, and medical image processing using a convolutional neural network (CNN) is giving excellent outcomes in this capacity. CNN uses the image fragments to train the data and classify them into tumor types. Hierarchical Deep Learning-Based Brain Tumor (HDL2BT) classification is proposed with the help of CNN for the detection and classification of brain tumors. The proposed system categorizes the tumor into four types: glioma, meningioma, pituitary, and no-tumor. The suggested model achieves 92.13% precision and a miss rate of 7.87%, being superior to earlier methods for detecting and segmentation brain tumors. The proposed system will provide clinical assistance in the area of medicine.
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14
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Fawzi A, Achuthan A, Belaton B. Brain Image Segmentation in Recent Years: A Narrative Review. Brain Sci 2021; 11:1055. [PMID: 34439674 PMCID: PMC8392552 DOI: 10.3390/brainsci11081055] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 07/10/2021] [Accepted: 07/19/2021] [Indexed: 11/17/2022] Open
Abstract
Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted. This has indirectly led to an increased demand for automated brain segmentation solutions to assist medical experts in early diagnosis and treatment interventions. This paper aims to present a critical review of the recent trend in segmentation and classification methods for brain magnetic resonance images. Various segmentation methods ranging from simple intensity-based to high-level segmentation approaches such as machine learning, metaheuristic, deep learning, and hybridization are included in the present review. Common issues, advantages, and disadvantages of brain image segmentation methods are also discussed to provide a better understanding of the strengths and limitations of existing methods. From this review, it is found that deep learning-based and hybrid-based metaheuristic approaches are more efficient for the reliable segmentation of brain tumors. However, these methods fall behind in terms of computation and memory complexity.
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Affiliation(s)
| | - Anusha Achuthan
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia; (A.F.); (B.B.)
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15
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Lam C, Tso CF, Green-Saxena A, Pellegrini E, Iqbal Z, Evans D, Hoffman J, Calvert J, Mao Q, Das R. Semi-supervised deep learning from time series clinical data for acute respiratory distress syndrome prediction: model development and validation study. JMIR Form Res 2021; 5:e28028. [PMID: 34398784 PMCID: PMC8447921 DOI: 10.2196/28028] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 06/18/2021] [Accepted: 08/01/2021] [Indexed: 11/23/2022] Open
Abstract
Background A high number of patients who are hospitalized with COVID-19 develop acute respiratory distress syndrome (ARDS). Objective In response to the need for clinical decision support tools to help manage the next pandemic during the early stages (ie, when limited labeled data are present), we developed machine learning algorithms that use semisupervised learning (SSL) techniques to predict ARDS development in general and COVID-19 populations based on limited labeled data. Methods SSL techniques were applied to 29,127 encounters with patients who were admitted to 7 US hospitals from May 1, 2019, to May 1, 2021. A recurrent neural network that used a time series of electronic health record data was applied to data that were collected when a patient’s peripheral oxygen saturation level fell below the normal range (<97%) to predict the subsequent development of ARDS during the remaining duration of patients’ hospital stay. Model performance was assessed with the area under the receiver operating characteristic curve and area under the precision recall curve of an external hold-out test set. Results For the whole data set, the median time between the first peripheral oxygen saturation measurement of <97% and subsequent respiratory failure was 21 hours. The area under the receiver operating characteristic curve for predicting subsequent ARDS development was 0.73 when the model was trained on a labeled data set of 6930 patients, 0.78 when the model was trained on the labeled data set that had been augmented with the unlabeled data set of 16,173 patients by using SSL techniques, and 0.84 when the model was trained on the entire training set of 23,103 labeled patients. Conclusions In the context of using time-series inpatient data and a careful model training design, unlabeled data can be used to improve the performance of machine learning models when labeled data for predicting ARDS development are scarce or expensive.
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Affiliation(s)
- Carson Lam
- Dascena, Inc., 12333 Sowden Rd Ste B PMB 65148, Houston, US
| | - Chak Foon Tso
- Dascena, Inc., 12333 Sowden Rd Ste B PMB 65148, Houston, US
| | | | | | - Zohora Iqbal
- Dascena, Inc., 12333 Sowden Rd Ste B PMB 65148, Houston, US
| | - Daniel Evans
- Dascena, Inc., 12333 Sowden Rd Ste B PMB 65148, Houston, US
| | - Jana Hoffman
- Dascena, Inc., 12333 Sowden Rd Ste B PMB 65148, Houston, US
| | - Jacob Calvert
- Dascena, Inc., 12333 Sowden Rd Ste B PMB 65148, Houston, US
| | - Qingqing Mao
- Dascena, Inc., 12333 Sowden Rd Ste B PMB 65148, Houston, US
| | - Ritankar Das
- Dascena, Inc., 12333 Sowden Rd Ste B PMB 65148, Houston, US
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16
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Gridach M. PyDiNet: Pyramid Dilated Network for medical image segmentation. Neural Netw 2021; 140:274-281. [PMID: 33839599 DOI: 10.1016/j.neunet.2021.03.023] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 03/08/2021] [Accepted: 03/12/2021] [Indexed: 12/18/2022]
Abstract
Medical image segmentation is an important step in many generic applications such as population analysis and, more accessible, can be made into a crucial tool in diagnosis and treatment planning. Previous approaches are based on two main architectures: fully convolutional networks and U-Net-based architecture. These methods rely on multiple pooling and striding layers leading to the loss of important spatial information and fail to capture details in medical images. In this paper, we propose a novel neural network called PyDiNet (Pyramid Dilated Network) to capture small and complex variations in medical images while preserving spatial information. To achieve this goal, PyDiNet uses a newly proposed pyramid dilated module (PDM), which consists of multiple dilated convolutions stacked in parallel. We combine several PDM modules to form the final PyDiNet architecture. We applied the proposed PyDiNet to different medical image segmentation tasks. Experimental results show that the proposed model achieves new state-of-the-art performance on three medical image segmentation benchmarks. Furthermore, PyDiNet was very competitive on the 2020 Endoscopic Artifact Detection challenge.
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Affiliation(s)
- Mourad Gridach
- Department of Computer Science, High Institute of Technology, Agadir, Morocco.
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17
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Pang S, Du A, Orgun MA, Wang Y, Yu Z. Tumor attention networks: Better feature selection, better tumor segmentation. Neural Netw 2021; 140:203-222. [PMID: 33780873 DOI: 10.1016/j.neunet.2021.03.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 01/15/2021] [Accepted: 03/05/2021] [Indexed: 02/01/2023]
Abstract
Compared with the traditional analysis of computed tomography scans, automatic liver tumor segmentation can supply precise tumor volumes and reduce the inter-observer variability in estimating the tumor size and the tumor burden, which could further assist physicians to make better therapeutic choices for hepatic diseases and monitoring treatment. Among current mainstream segmentation approaches, multi-layer and multi-kernel convolutional neural networks (CNNs) have attracted much attention in diverse biomedical/medical image segmentation tasks with remarkable performance. However, an arbitrary stacking of feature maps makes CNNs quite inconsistent in imitating the cognition and the visual attention of human beings for a specific visual task. To mitigate the lack of a reasonable feature selection mechanism in CNNs, we exploit a novel and effective network architecture, called Tumor Attention Networks (TA-Net), for mining adaptive features by embedding Tumor Attention layers with multi-functional modules to assist the liver tumor segmentation task. In particular, each tumor attention layer can adaptively highlight valuable tumor features and suppress unrelated ones among feature maps from 3D and 2D perspectives. Moreover, an analysis of visualization results illustrates the effectiveness of our tumor attention modules and the interpretability of CNNs for liver tumor segmentation. Furthermore, we explore different arrangements of skip connections in information fusion. A deep ablation study is also conducted to illustrate the effects of different attention strategies for hepatic tumors. The results of extensive experiments demonstrate that the proposed TA-Net increases the liver tumor segmentation performance with a lower computation cost and a small parameter overhead over the state-of-the-art methods, under various evaluation metrics on clinical benchmark data. In addition, two additional medical image datasets are used to evaluate generalization capability of TA-Net, including the comparison with general semantic segmentation methods and a non-tumor segmentation task. All the program codes have been released at https://github.com/shuchao1212/TA-Net.
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Affiliation(s)
- Shuchao Pang
- Department of Computing, Macquarie University, Sydney, NSW 2109, Australia.
| | - Anan Du
- School of Electrical and Data Engineering, University of Technology Sydney, NSW 2007, Australia.
| | - Mehmet A Orgun
- Department of Computing, Macquarie University, Sydney, NSW 2109, Australia; Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa 999078, Macau, China.
| | - Yunyun Wang
- Department of Anesthesiology, China-Japan Union Hospital of Jilin University, Changchun 130012, China.
| | - Zhenmei Yu
- School of Data and Computer Science, Shandong Women's University, Jinan 250014, China.
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18
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A level set method based on domain transformation and bias correction for MRI brain tumor segmentation. J Neurosci Methods 2021; 352:109091. [PMID: 33515604 DOI: 10.1016/j.jneumeth.2021.109091] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 01/18/2021] [Accepted: 01/21/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Intensity inhomogeneity is one of the common artifacts in image processing. This artifact makes image segmentation more challenging and adversely affects the performance of intensity-based image processing algorithms. NEW METHOD In this paper, a novel region-based level set method is proposed for segmenting the images with intensity inhomogeneity with applications to brain tumor segmentation in magnetic resonance imaging (MRI) scans. For this purpose, the inhomogeneous regions are first modeled as Gaussian distributions with different means and variances, and then transferred into a new domain, where preserves the Gaussian intensity distribution of each region but with better separation. Moreover, our method can perform bias field correction. To this end, the bias field is represented by a linear combination of smooth base functions that enables better intensity inhomogeneity modeling. Therefore, level set fundamental formulation and bias field are modified in the proposed approach. RESULTS To assess the performance of the proposed method, different inhomogeneous images, including synthetic images as well as real brain magnetic resonance images from BraTS 2017 dataset are segmented. Being evaluated by Dice, Jaccard, Sensitivity, and Specificity metrics, the results show that the proposed method suppresses the side effect of the over-smoothing object boundary and it has good accuracy in the segmentation of images with extreme intensity non-uniformity. The mean values of these metrics in brain tumor segmentation are 0.86 ± 0.03, 0.77 ± 0.05, 0.94 ± 0.04, 0.99 ± 0.003, respectively. COMPARISON WITH EXISTING METHOD(S) Our method were compared with six state-of-the-art image segmentation methods: Chan-Vese (CV), Local Intensity Clustering (LIC), Local iNtensity Clustering (LINC), Global inhomogeneous intensity clustering (GINC), Multiplicative Intrinsic Component Optimization (MICO), and Local Statistical Active Contour Model (LSACM) models. We used qualitative and quantitative comparison methods for segmenting synthetic and real images. Experiments indicate that our proposed method is robust to noise and intensity non-uniformity and outperforms other state-of-the-art segmentation methods in terms of bias field correction, noise resistance, and segmentation accuracy. CONCLUSIONS Experimental results show that the proposed model is capable of accurate segmentation and bias field estimation simultaneously. The proposed model suppresses the side effect of the over-smoothing object boundary. Moreover, our model has good accuracy in the segmentation of images with extreme intensity non-uniformity.
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Pardoe HR, Antony AR, Hetherington H, Bagić AI, Shepherd TM, Friedman D, Devinsky O, Pan J. High resolution automated labeling of the hippocampus and amygdala using a 3D convolutional neural network trained on whole brain 700 μm isotropic 7T MP2RAGE MRI. Hum Brain Mapp 2021; 42:2089-2098. [PMID: 33491831 PMCID: PMC8046047 DOI: 10.1002/hbm.25348] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 01/04/2021] [Accepted: 01/09/2021] [Indexed: 12/14/2022] Open
Abstract
Image labeling using convolutional neural networks (CNNs) are a template-free alternative to traditional morphometric techniques. We trained a 3D deep CNN to label the hippocampus and amygdala on whole brain 700 μm isotropic 3D MP2RAGE MRI acquired at 7T. Manual labels of the hippocampus and amygdala were used to (i) train the predictive model and (ii) evaluate performance of the model when applied to new scans. Healthy controls and individuals with epilepsy were included in our analyses. Twenty-one healthy controls and sixteen individuals with epilepsy were included in the study. We utilized the recently developed DeepMedic software to train a CNN to label the hippocampus and amygdala based on manual labels. Performance was evaluated by measuring the dice similarity coefficient (DSC) between CNN-based and manual labels. A leave-one-out cross validation scheme was used. CNN-based and manual volume estimates were compared for the left and right hippocampus and amygdala in healthy controls and epilepsy cases. The CNN-based technique successfully labeled the hippocampus and amygdala in all cases. Mean DSC = 0.88 ± 0.03 for the hippocampus and 0.8 ± 0.06 for the amygdala. CNN-based labeling was independent of epilepsy diagnosis in our sample (p = .91). CNN-based volume estimates were highly correlated with manual volume estimates in epilepsy cases and controls. CNNs can label the hippocampus and amygdala on native sub-mm resolution MP2RAGE 7T MRI. Our findings suggest deep learning techniques can advance development of morphometric analysis techniques for high field strength, high spatial resolution brain MRI.
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Affiliation(s)
- Heath R Pardoe
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Arun Raj Antony
- Department of Neurology, University of Pittsburgh Medical School, Pittsburgh, Pennsylvania, USA
| | - Hoby Hetherington
- Department of Neurology, University of Pittsburgh Medical School, Pittsburgh, Pennsylvania, USA
| | - Anto I Bagić
- Department of Neurology, University of Pittsburgh Medical School, Pittsburgh, Pennsylvania, USA
| | - Timothy M Shepherd
- Department of Radiology, NYU Grossman School of Medicine, New York, New York, USA
| | - Daniel Friedman
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA
| | - Jullie Pan
- Department of Neurology, University of Pittsburgh Medical School, Pittsburgh, Pennsylvania, USA
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20
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Cao X, Chen H, Li Y, Peng Y, Wang S, Cheng L. Uncertainty Aware Temporal-Ensembling Model for Semi-Supervised ABUS Mass Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:431-443. [PMID: 33021936 DOI: 10.1109/tmi.2020.3029161] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Accurate breast mass segmentation of automated breast ultrasound (ABUS) images plays a crucial role in 3D breast reconstruction which can assist radiologists in surgery planning. Although the convolutional neural network has great potential for breast mass segmentation due to the remarkable progress of deep learning, the lack of annotated data limits the performance of deep CNNs. In this article, we present an uncertainty aware temporal ensembling (UATE) model for semi-supervised ABUS mass segmentation. Specifically, a temporal ensembling segmentation (TEs) model is designed to segment breast mass using a few labeled images and a large number of unlabeled images. Considering the network output contains correct predictions and unreliable predictions, equally treating each prediction in pseudo label update and loss calculation may degrade the network performance. To alleviate this problem, the uncertainty map is estimated for each image. Then an adaptive ensembling momentum map and an uncertainty aware unsupervised loss are designed and integrated with TEs model. The effectiveness of the proposed UATE model is mainly verified on an ABUS dataset of 107 patients with 170 volumes, including 13382 2D labeled slices. The Jaccard index (JI), Dice similarity coefficient (DSC), pixel-wise accuracy (AC) and Hausdorff distance (HD) of the proposed method on testing set are 63.65%, 74.25%, 99.21% and 3.81mm respectively. Experimental results demonstrate that our semi-supervised method outperforms the fully supervised method, and get a promising result compared with existing semi-supervised methods.
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21
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Chaves H, Dorr F, Costa ME, Serra MM, Slezak DF, Farez MF, Sevlever G, Yañez P, Cejas C. Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL. J Neuroradiol 2020; 48:147-156. [PMID: 33137334 DOI: 10.1016/j.neurad.2020.10.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 09/13/2020] [Accepted: 10/19/2020] [Indexed: 01/22/2023]
Abstract
BACKGROUND AND PURPOSE There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC). MATERIALS AND METHODS Sixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV). RESULTS Whole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94-0.97)) than FreeSurfer and CAT12 (0.92 (0.88-0.96)) and FSL (0.87 (0.79-0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20-3.13% vs. mean CV 1.05, range 0.21-3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49-5.91% vs. mean CV 3.84, range 2.62-5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively. CONCLUSION Based on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.
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Affiliation(s)
- Hernán Chaves
- Diagnostic Imaging Department, Fleni, Buenos Aires, Argentina; Entelai, Buenos Aires, Argentina.
| | | | | | - María Mercedes Serra
- Diagnostic Imaging Department, Fleni, Buenos Aires, Argentina; Entelai, Buenos Aires, Argentina
| | - Diego Fernández Slezak
- Entelai, Buenos Aires, Argentina; Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina; Instituto en Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Mauricio F Farez
- Entelai, Buenos Aires, Argentina; Neurology Department, Fleni, Buenos Aires, Argentina; Center for Research on Neuroimmunological Diseases (CIEN), Fleni, Buenos Aires, Argentina; Center for Biostatistics, Epidemiology and Public Health (CEBES), Fleni, Buenos Aires, Argentina
| | | | - Paulina Yañez
- Diagnostic Imaging Department, Fleni, Buenos Aires, Argentina
| | - Claudia Cejas
- Diagnostic Imaging Department, Fleni, Buenos Aires, Argentina
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Henschel L, Conjeti S, Estrada S, Diers K, Fischl B, Reuter M. FastSurfer - A fast and accurate deep learning based neuroimaging pipeline. Neuroimage 2020; 219:117012. [PMID: 32526386 PMCID: PMC7898243 DOI: 10.1016/j.neuroimage.2020.117012] [Citation(s) in RCA: 170] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 05/29/2020] [Accepted: 05/31/2020] [Indexed: 02/01/2023] Open
Abstract
Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands or tens of thousands of individuals. In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer's anatomical segmentation including surface reconstruction and cortical parcellation. To this end, we introduce an advanced deep learning architecture capable of whole-brain segmentation into 95 classes. The network architecture incorporates local and global competition via competitive dense blocks and competitive skip pathways, as well as multi-slice information aggregation that specifically tailor network performance towards accurate segmentation of both cortical and subcortical structures. Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface. This approach provides a full FreeSurfer alternative for volumetric analysis (in under 1 min) and surface-based thickness analysis (within only around 1 h runtime). For sustainability of this approach we perform extensive validation: we assert high segmentation accuracy on several unseen datasets, measure generalizability and demonstrate increased test-retest reliability, and high sensitivity to group differences in dementia.
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Affiliation(s)
- Leonie Henschel
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Sailesh Conjeti
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Santiago Estrada
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Kersten Diers
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Bruce Fischl
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
| | - Martin Reuter
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
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Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10165640] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Accurate and timely landslide susceptibility mapping (LSM) is essential to effectively reduce the risk of landslide. In recent years, deep learning has been successfully applied to landslide susceptibility assessment due to the strong ability of fitting. However, in actual applications, the number of labeled samples is usually not sufficient for the training component. In this paper, a deep neural network model based on semi-supervised learning (SSL-DNN) for landslide susceptibility is proposed, which makes full use of a large number of spatial information (unlabeled data) with limited labeled data in the region to train the mode. Taking Jiaohe County in Jilin Province, China as an example, the landslide inventory from 2000 to 2017 was collected and 12 metrological, geographical, and human explanatory factors were compiled. Meanwhile, supervised models such as deep neural network (DNN), support vector machine (SVM), and logistic regression (LR) were implemented for comparison. Then, the landslide susceptibility was plotted and a series of evaluation tools such as class accuracy, predictive rate curves (AUC), and information gain ratio (IGR) were calculated to compare the prediction of models and factors. Experimental results indicate that the proposed SSL-DNN model (AUC = 0.898) outperformed all the comparison models. Therefore, semi-supervised deep learning could be considered as a potential approach for LSM.
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Ishii S, Lee S, Urakubo H, Kume H, Kasai H. Generative and discriminative model-based approaches to microscopic image restoration and segmentation. Microscopy (Oxf) 2020; 69:79-91. [PMID: 32215571 PMCID: PMC7141893 DOI: 10.1093/jmicro/dfaa007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 02/02/2020] [Accepted: 02/17/2020] [Indexed: 11/14/2022] Open
Abstract
Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. However, the application of ML to microscopic images is limited as microscopic images are often 3D/4D, that is, the image sizes can be very large, and the images may suffer from serious noise generated due to optics. In this review, three types of feature reconstruction applications to microscopic images are discussed, which fully utilize the recent advancements in ML technologies. First, multi-frame super-resolution is introduced, based on the formulation of statistical generative model-based techniques such as Bayesian inference. Second, data-driven image restoration is introduced, based on supervised discriminative model-based ML technique. In this application, CNNs are demonstrated to exhibit preferable restoration performance. Third, image segmentation based on data-driven CNNs is introduced. Image segmentation has become immensely popular in object segmentation based on electron microscopy (EM); therefore, we focus on EM image processing.
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Affiliation(s)
- Shin Ishii
- Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
- ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan
- International Research Center for Neurointelligence, The University of Tokyo, Tokyo 113-0033, Japan
| | - Sehyung Lee
- Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
- International Research Center for Neurointelligence, The University of Tokyo, Tokyo 113-0033, Japan
| | - Hidetoshi Urakubo
- Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
| | - Hideaki Kume
- International Research Center for Neurointelligence, The University of Tokyo, Tokyo 113-0033, Japan
- Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Haruo Kasai
- International Research Center for Neurointelligence, The University of Tokyo, Tokyo 113-0033, Japan
- Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
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Tiwari A, Srivastava S, Pant M. Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.11.020] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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