Dataset Information

Deep learning-based segmentation of multisite disease in ovarian cancer.

ABSTRACT:

Purpose

To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.

Methods

A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established "no-new-Net" framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test.

Results

Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10^-7, 3 × 10^-4, 4 × 10^-2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10^-3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions.

Conclusion

Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions.

Relevance statement

Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines.

Key points

• The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines.

SUBMITTER: Buddenkotte T

PROVIDER: S-EPMC10700248 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

Deep learning-based segmentation of multisite disease in ovarian cancer.

Buddenkotte Thomas T Rundo Leonardo L Woitek Ramona R Escudero Sanchez Lorena L Beer Lucian L Crispin-Ortuzar Mireia M Etmann Christian C Mukherjee Subhadip S Bura Vlad V McCague Cathal C Sahin Hilal H Pintican Roxana R Zerunian Marta M Allajbeu Iris I Singh Naveena N Sahdev Anju A Havrilesky Laura L Cohn David E DE Bateman Nicholas W NW Conrads Thomas P TP Darcy Kathleen M KM Maxwell G Larry GL Freymann John B JB Öktem Ozan O Brenton James D JD Sala Evis E Schönlieb Carola-Bibiane CB

European radiology experimental 20231207 1

<h4>Purpose</h4>To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.<h4>Methods</h4>A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established "no-new-Net" framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected ...[more]

PMID: 38057616

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Project description:ObjectiveWe aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods.Materials and methodsWe collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume.ResultsThe original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998).ConclusionDeep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

Dataset Information

Deep learning-based segmentation of multisite disease in ovarian cancer.

Purpose

Methods

Results

Conclusion

Relevance statement

Key points

Publications

Deep learning-based segmentation of multisite disease in ovarian cancer.

Similar Datasets

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