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The Segmentation of Multiple Types of Uterine Lesions in Magnetic Resonance Images Using a Sequential Deep Learning Method with Image-Level Annotations

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Abstract

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.

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Data Availability

Data that support the findings of this study are available on request from the corresponding author. Data are not publicly available due to privacy or ethical restrictions.

Abbreviations

WSSS:

Weakly supervised semantic segmentation

FSSS:

Fully supervised semantic segmentation

CAM:

Class activation map

GAP:

Global average pooling

PCM:

Pixel correlation module

ReCAM:

Class re-activation map

IRNet:

Inter-pixel relation network

GT:

Ground truth

SCE:

Softmax cross entropy

BCE:

Binary cross entropy

DSC:

Dice similarity coefficient

HD:

Hausdorff distance

ASSD:

Average symmetric surface distance

EC:

Endometrial cancer

UL:

Uterine leiomyoma

EP:

Endometrial polyps

AHE:

Atypical hyperplasia of endometrium

ROI:

Region of interest

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Funding

This study has received funding by the National Natural Science Foundation of China (No. 82003843), Natural Science Foundation of Liaoning Province (No. 2020-BS-221), and Dalian Medical Science Research Program (No.2211023).

Author information

Authors and Affiliations

  1. Department of Gynecology, Dalian Women and Children’s Medical Group, Dalian, 116033, China

    Yu-meng Cui, Hua-li Wang, Rui Cao, Hong Bai, Dan Sun & Jiu-xiang Feng

  2. School of Food Science and Engineering, Dalian Ocean University, Dalian, 116023, China

    Xue-feng Lu

Authors
  1. Yu-meng Cui
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  2. Hua-li Wang
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  3. Rui Cao
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  4. Hong Bai
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  5. Dan Sun
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Contributions

Yu-meng Cui: conceptualization; methodology; writing; funding acquisition; Hua-li Wang: conceptualization; resources; supervision; Rui Cao: resources; supervision; Hong Bai: methodology; validation; statistical analysis; Dan Sun: data curation; project administration; Jiu-Xiang Feng: data curation; project administration; Xue-feng Lu: conceptualization; review and editing; coding; funding acquisition.

Corresponding author

Correspondence to Xue-feng Lu.

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Ethics Approval

This study was approved by the Institutional Review Board of Dalian Women and Children’s Medical Group before patient information was accessed.

Consent to Participate

This study was performed under a waiver for informed consent due to the retrospective nature of the analysis, the anonymity of the data.

Competing Interests

The authors declare no competing interests.

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Cui, Ym., Wang, Hl., Cao, R. et al. The Segmentation of Multiple Types of Uterine Lesions in Magnetic Resonance Images Using a Sequential Deep Learning Method with Image-Level Annotations. J Digit Imaging. Inform. med. 37, 374–385 (2024). https://doi.org/10.1007/s10278-023-00931-9

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  • DOI: https://doi.org/10.1007/s10278-023-00931-9

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