The Segmentation of Multiple Types of Uterine Lesions in Magnetic Resonance Images Using a Sequential Deep Learning Method with Image-Level Annotations.
Fully supervised medical image segmentation methods use pixel-level labels to achieve good results, but obtaining such large-scale, high-quality labels is cumbersome and time consuming. This study aimed to develop a weakly supervised model that only used image-level labels to achieve automatic segme...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 1; pp. 374 - 386 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175966522&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175966522 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2024 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175966522 175966522 175966522 10.1007/s10278-023-00931-9 175966522 ppf: 374 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: The Segmentation of Multiple Types of Uterine Lesions in Magnetic Resonance Images Using a Sequential Deep Learning Method with Image-Level Annotations. aug: au: Cui, Yu-meng Wang, Hua-li Cao, Rui Bai, Hong Sun, Dan Feng, Jiu-xiang Lu, Xue-feng affil: Department of Gynecology, Dalian Women and Children's Medical Group, 116033, Dalian, China sug: subj: Uterine Neoplasms Diagnosis Magnetic Resonance Imaging Deep Learning Utilization Image Processing, Computer Assisted Neural Networks (Computer) Sensitivity and Specificity Human Female Adult Middle Age Aged Retrospective Design Endometrial Neoplasms Leiomyoma Polyps Hyperplasia Descriptive Statistics Comparative Studies Uterine Diseases Diagnosis, Computer Assisted Early Detection of Cancer Women's Health Oncologic Care Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female ab: Fully supervised medical image segmentation methods use pixel-level labels to achieve good results, but obtaining such large-scale, high-quality labels is cumbersome and time consuming. This study aimed to develop a weakly supervised model that only used image-level labels to achieve automatic segmentation of four types of uterine lesions and three types of normal tissues on magnetic resonance images. The MRI data of the patients were retrospectively collected from the database of our institution, and the T2-weighted sequence images were selected and only image-level annotations were made. The proposed two-stage model can be divided into four sequential parts: the pixel correlation module, the class re-activation map module, the inter-pixel relation network module, and the Deeplab v3 + module. The dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average symmetric surface distance (ASSD) were employed to evaluate the performance of the model. The original dataset consisted of 85,730 images from 316 patients with four different types of lesions (i.e., endometrial cancer, uterine leiomyoma, endometrial polyps, and atypical hyperplasia of endometrium). A total number of 196, 57, and 63 patients were randomly selected for model training, validation, and testing. After being trained from scratch, the proposed model showed a good segmentation performance with an average DSC of 83.5%, HD of 29.3 mm, and ASSD of 8.83 mm, respectively. As far as the weakly supervised methods using only image-level labels are concerned, the performance of the proposed model is equivalent to the state-of-the-art weakly supervised methods. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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