1
|
Weakly-supervised localization and classification of biomarkers in OCT images with integrated reconstruction and attention. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Liu X, Yuan Q, Gao Y, He K, Wang S, Tang X, Tang J, Shen D. Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images. PATTERN RECOGNITION 2022; 122:108341. [PMID: 34565913 PMCID: PMC8452156 DOI: 10.1016/j.patcog.2021.108341] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/23/2021] [Accepted: 09/18/2021] [Indexed: 05/19/2023]
Abstract
Segmentation of infections from CT scans is important for accurate diagnosis and follow-up in tackling the COVID-19. Although the convolutional neural network has great potential to automate the segmentation task, most existing deep learning-based infection segmentation methods require fully annotated ground-truth labels for training, which is time-consuming and labor-intensive. This paper proposed a novel weakly supervised segmentation method for COVID-19 infections in CT slices, which only requires scribble supervision and is enhanced with the uncertainty-aware self-ensembling and transformation-consistent techniques. Specifically, to deal with the difficulty caused by the shortage of supervision, an uncertainty-aware mean teacher is incorporated into the scribble-based segmentation method, encouraging the segmentation predictions to be consistent under different perturbations for an input image. This mean teacher model can guide the student model to be trained using information in images without requiring manual annotations. On the other hand, considering the output of the mean teacher contains both correct and unreliable predictions, equally treating each prediction in the teacher model may degrade the performance of the student network. To alleviate this problem, the pixel level uncertainty measure on the predictions of the teacher model is calculated, and then the student model is only guided by reliable predictions from the teacher model. To further regularize the network, a transformation-consistent strategy is also incorporated, which requires the prediction to follow the same transformation if a transform is performed on an input image of the network. The proposed method has been evaluated on two public datasets and one local dataset. The experimental results demonstrate that the proposed method is more effective than other weakly supervised methods and achieves similar performance as those fully supervised.
Collapse
Affiliation(s)
- Xiaoming Liu
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China
| | - Quan Yuan
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China
| | - Yaozong Gao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Kelei He
- Medical School, Nanjing University, Nanjing, China
- National Institute of Healthcare Data Science at Nanjing University, Nanjing, China
| | - Shuo Wang
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, China
| | - Xiao Tang
- Department of Medical Imaging, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Jinshan Tang
- Department of Health Administration and Policy, George Mason University, Fairfax, VA, 22030, USA
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
- Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea
| |
Collapse
|
4
|
Wang T, Sun B, Jiang C, Weng H, Chu X. Kernel alignment-based three-way clustering on attribute space and its application in stroke risk identification. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-021-01478-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
5
|
CTSVM: A robust twin support vector machine with correntropy-induced loss function for binary classification problems. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.01.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
9
|
Mohanty F, Rup S, Dash B, Majhi B, Swamy MNS. Digital mammogram classification using 2D-BDWT and GLCM features with FOA-based feature selection approach. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04186-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|