Correction: Deep learning accurately and reliably segments pelvic vascular structure in CT scans of gynecologic cancer patients - Report - MDSpire
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Correction: Accurate and Reliable Segmentation of Pelvic Vascular Structures in CT Imaging of Gynecologic Cancer Patients Using Deep Learning Techniques

  • By

  • Hyeonseung Kim

  • Min Jin Jeong

  • Kyoyeong Koo

  • Youn Jin Choi

  • Eun Seo Heo

  • Woohyun Nam

  • Sangyun Kang

  • U-Young Lee

  • Yi-Suk Kim

  • Keun Ho Lee

  • Chan-Ung Park

  • August 24, 2026

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Correction: Accurate and Reliable Segmentation of Pelvic Vascular Structures

Background

Accurate imaging and segmentation of pelvic vascular structures are crucial for the management of gynecologic cancers. Deep learning techniques have shown promise in enhancing the precision of these imaging processes.

Data Highlights

No numerical data or trial results were presented in the correction notice.

Key Findings

  • The funding statement for the original study was incorrect.
  • The National Cancer Center (NCC) is the correct funder of the study.
  • The study focuses on deep learning techniques for segmenting pelvic vascular structures.
  • Accurate segmentation is important for gynecologic cancer management.
  • The original article has been updated to reflect these corrections.

Clinical Implications

Healthcare professionals should be aware of the importance of accurate funding attribution in research publications.

Conclusion

The correction emphasizes the need for precise funding disclosures in research.

Related Resources & Content

  1. Kim H, Jeong MJ, Koo KY, et al., Front. Oncol., 2026 -- Correction: Accurate and Reliable Segmentation of Pelvic Vascular Structures in CT Imaging of Gynecologic Cancer Patients Using Deep Learning Techniques
  2. Deep Learning Approaches for Automated Image Segmentation and Evaluation of Treatment Outcomes in Metastatic Ovarian Cancer
  3. Frontiers in Digital Health — Adapting DeepLabV3+ for biopsy cervical cancer lesion segmentation
  4. Techniques in Coloproctology — Automated Identification of Male Pelvic Floor Soft Tissue Anatomy for Simulation and Morphological Evaluation in Lower Rectal Cancer Procedures
  5. npj Digital Medicine — Multimodal Integration of Endoscopic and Radiomic Data for Predicting Survival Outcomes in Colorectal Cancer
  6. European Society of Gynaecological Oncology resource-stratified guidelines for the management of patients with cervical cancer - ScienceDirect
  7. SEOM-GEICO clinical guidelines on endometrial cancer (2025) - PMC
  8. Revised 2025
  9. FIGO 2025 Gynecologic Cancers: An Ultrasound-Focused Imaging Update - PubMed
  10. Prospective comparison of diagnostic accuracy of ultrasound, PET/CT and DW-MRI for preoperative assessment of pelvic lymph nodes in cervical cancer patients: results of the CANNES trial - PubMed
  11. TRUST: Trial of radical upfront surgical therapy in advanced ovarian cancer (ENGOT ov33/AGO‐OVAR OP7). | Journal of Clinical Oncology
  12. Consensus Guidelines for Delineation of Clinical Target Volumes for Intensity-Modulated Radiotherapy for Intact Cervical Cancer: An Update - PMC
  13. ACR Approves First Practice Parameter for Imaging Artificial Intelligence

Original Source(s)

Correction: Deep learning accurately and reliably segments pelvic vascular structure in CT scans of gynecologic cancer patients

  • by Hyeonseung Kim, Min Jin Jeong, Kyoyeong Koo, Youn Jin Choi, Eun Seo Heo, Woohyun Nam, Sangyun Kang, U-Young Lee, Yi-Suk Kim, Keun Ho Lee, Chan-Ung Park

  • August 24, 2026

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