# Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{7}

^{8}

^{*}

^{†}

## Abstract

**:**

## Simple Summary

## Abstract

## 1. Introduction

## 2. Methods

#### 2.1. MRI Acquisitions

#### 2.2. Inclusion Criteria

#### 2.3. Manual Tumor Segmentation

#### 2.4. Major Processing Steps

#### 2.5. Evaluation of Segmentation Performance

#### 2.6. Implementation Details

`Imagedata`[26] for the reading and writing of image data between DICOM (https://dicomstandard.org, (accessed on 1 March 2018)) or NIfTI file format and

`NumPy`arrays [27]. An in-house developed algorithm applying the geometric coordinate transformation specified within the DICOM image header was used for spatial alignment of the DWI data (ADC map and high b-value image) with the T2-weighted image using trilinear interpolation. After transformation, image voxel data for each patient was specified on the same spatial grid. Out-of-grid extrapolation values were set to zero.

`RegularGridInterpolator`from

`SciPy`[30]. The interpolation method was ‘trilinear’ for the MRI data, and ‘nearest neighbor’ for the binary mask data. They were channel-wise normalized using z-normalization (i.e., zero mean and unit standard deviation), and resized to 304 × 304 × 144 dimensions using either cropping or zero padding. The image data were of different matrix sizes, and the amount of cropping and padding was therefore different between data sets. We used data parallelism in PyTorch to train our model, a batch size of 4, and trained the model for 60 epochs using Dice loss function [31] on four NVIDIA Tesla V100 32 GB GPUs. We employed a Ranger optimizer [32] with an initial learning rate of 0.1, rapidly decreasing during the final few epochs using a cosine annealing scheduler, an idea that is related to the concept of super-convergence [33]. For data augmentation, we used random zooming by a factor in the range [1, 1.2], and random elastic deformations with 5 control points along each dimension of the coarse grid with a maximum displacement set f to 4 along each direction at each control point. The transformations were performed on the fly during training, with a probability set to 0.2 for each transformation. The weights of our final model were selected based on a callback that monitored the DSC on the validation data after each epoch, with the condition of saving the model if the performance of the validation data was improved by at least 0.005 × DSC from the currently best model. Source code used in this work is openly available via GitHub (https://github.com/MMIV-DL/cervical-cancer-segmentation-2022, accessed on 21 March 2022).

## 3. Results

#### 3.1. Train and Validation Metrics

#### 3.2. Performance in Terms of DSC and HD

#### 3.3. Performance in Terms of Reported Tumor Volume

## 4. Discussion

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin.
**2021**, 71, 209–249. [Google Scholar] [CrossRef] [PubMed] - Varghese, B.A.; Cen, S.Y.; Hwang, D.H.; Duddalwar, V.A. Texture analysis of imaging: What radiologists need to know. Am. J. Roentgenol.
**2019**, 212, 520–528. [Google Scholar] [CrossRef] [PubMed] - Zhang, Q.; Yu, X.; Ouyang, H.; Zhang, J.; Chen, S.; Xie, L.; Zhao, X. Whole-tumor texture model based on diffusion kurtosis imaging for assessing cervical cancer: A preliminary study. Eur. Radiol.
**2021**, 31, 5576–5585. [Google Scholar] [CrossRef] [PubMed] - Xiao, M.; Ma, F.; Li, Y.; Li, Y.; Li, M.; Zhang, G.; Qiang, J. Multiparametric MRI-based radiomics nomogram for predicting lymph node metastasis in early-stage cervical cancer. J. Magn. Reson. Imaging
**2020**, 52, 885–896. [Google Scholar] [CrossRef] - Wang, T.; Gao, T.; Guo, H.; Wang, Y.; Zhou, X.; Tian, J.; Huang, L.; Zhang, M. Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram. Eur. Radiol.
**2020**, 30, 3585–3593. [Google Scholar] [CrossRef] - Sun, C.; Tian, X.; Liu, Z.; Li, W.; Li, P.; Chen, J.; Zhang, W.; Fang, Z.; Du, P.; Duan, H.; et al. Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study. EBioMedicine
**2019**, 46, 160–169. [Google Scholar] [CrossRef] [Green Version] - Zhou, Y.; Gu, H.L.; Zhang, X.L.; Tian, Z.F.; Xu, X.Q.; Tang, W.W. Multiparametric magnetic resonance imaging-derived radiomics for the prediction of disease-free survival in early-stage squamous cervical cancer. Eur. Radiol.
**2021**, 32, 2540–2551. [Google Scholar] [CrossRef] - Lucia, F.; Visvikis, D.; Desseroit, M.C.; Miranda, O.; Malhaire, J.P.; Robin, P.; Pradier, O.; Hatt, M.; Schick, U. Prediction of outcome using pretreatment 18 F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy. Eur. J. Nucl. Med. Mol. Imaging
**2018**, 45, 768–786. [Google Scholar] [CrossRef] [Green Version] - Lucia, F.; Visvikis, D.; Vallières, M.; Desseroit, M.C.; Miranda, O.; Robin, P.; Bonaffini, P.A.; Alfieri, J.; Masson, I.; Mervoyer, A.; et al. External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy. Eur. J. Nucl. Med. Mol. Imaging
**2019**, 46, 864–877. [Google Scholar] [CrossRef] - Torheim, T.; Malinen, E.; Hole, K.H.; Lund, K.V.; Indahl, U.G.; Lyng, H.; Kvaal, K.; Futsaether, C.M. Autodelineation of cervical cancers using multiparametric magnetic resonance imaging and machine learning. Acta Oncol.
**2017**, 56, 806–812. [Google Scholar] [CrossRef] - Kano, Y.; Ikushima, H.; Sasaki, M.; Haga, A. Automatic contour segmentation of cervical cancer using artificial intelligence. J. Radiat. Res.
**2021**, 62, 934–944. [Google Scholar] [CrossRef] [PubMed] - Lin, Y.C.; Lin, C.H.; Lu, H.Y.; Chiang, H.J.; Wang, H.K.; Huang, Y.T.; Ng, S.H.; Hong, J.H.; Yen, T.C.; Lai, C.H.; et al. Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer. Eur. Radiol.
**2020**, 30, 1297–1305. [Google Scholar] [CrossRef] [PubMed] - Bnouni, N.; Rekik, I.; Rhim, M.S.; Amara, N.E.B. Context-Aware Synergetic Multiplex Network for Multi-organ Segmentation of Cervical Cancer MRI. In Proceedings of the International Workshop on Predictive Intelligence in Medicine, Lima, Peru, 8 October 2020; Springer: Berlin/Heidelberg, Germany, 2020; pp. 1–11. [Google Scholar]
- Renard, F.; Guedria, S.; Palma, N.D.; Vuillerme, N. Variability and reproducibility in deep learning for medical image segmentation. Sci. Rep.
**2020**, 10, 13724. [Google Scholar] [CrossRef] - Almeida, G.; Tavares, J.M.R. Deep learning in radiation oncology treatment planning for prostate cancer: A systematic review. J. Med. Syst.
**2020**, 44, 1–15. [Google Scholar] [CrossRef] - Lundervold, A.S.; Lundervold, A. An overview of deep learning in medical imaging focusing on MRI. Z. Med. Phys.
**2019**, 29, 102–127. [Google Scholar] [CrossRef] [PubMed] - Zhou, T.; Ruan, S.; Canu, S. A review: Deep learning for medical image segmentation using multi-modality fusion. Array
**2019**, 3, 100004. [Google Scholar] [CrossRef] - Ronneberger, O.; Fischer, P.; Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 5–9 October 2015; Springer: Berlin/Heidelberg, Germany, 2015; pp. 234–241. [Google Scholar]
- Kerfoot, E.; Clough, J.; Oksuz, I.; Lee, J.; King, A.P.; Schnabel, J.A. Left-ventricle quantification using residual U-Net. In Proceedings of the International Workshop on Statistical Atlases and Computational Models of the Heart, Granada, Spain, 16 September 2018; Springer: Berlin/Heidelberg, Germany, 2018; pp. 371–380. [Google Scholar]
- Yushkevich, P.A.; Piven, J.; Cody Hazlett, H.; Gimpel Smith, R.; Ho, S.; Gee, J.C.; Gerig, G. User-Guided 3D Active Contour Segmentation of Anatomical Structures: Significantly Improved Efficiency and Reliability. Neuroimage
**2006**, 31, 1116–1128. [Google Scholar] [CrossRef] [Green Version] - Cox, R.; Ashburner, J.; Breman, H.; Fissell, K.; Haselgrove, C.; Holmes, C.; Lancaster, J.; Rex, D.; Smith, S.; Woodward, J.; et al. A (sort of) new image data format standard: NiFTI-1. Presented at the 10th Annual Meeting of the Organization for Human Brain Mapping, Budapest, Hungary, 13–17 June 2004. [Google Scholar]
- Zhang, Y.; Chen, W.; Chen, Y.; Tang, X. A post-processing method to improve the white matter hyperintensity segmentation accuracy for randomly-initialized U-net. In Proceedings of the 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China, 19–21 November 2018; pp. 1–5. [Google Scholar]
- Kikinis, R.; Pieper, S.D.; Vosburgh, K.G. 3D Slicer: A platform for subject-specific image analysis, visualization, and clinical support. In Intraoperative Imaging and Image-Guided Therapy; Springer: Berlin/Heidelberg, Germany, 2014; pp. 277–289. [Google Scholar]
- Dice, L.R. Measures of the amount of ecologic association between species. Ecology
**1945**, 26, 297–302. [Google Scholar] [CrossRef] - Hausdorff, F. Grundzüge der Mengenlehre. In SSVM; Leipzig Viet: Leipzig, Germany, 1949. [Google Scholar]
- Andersen, E. Imagedata: A Python library to handle medical image data in NumPy array subclass Series. J. Open Source Softw. 2022, submitted.
- Harris, C.R.; Millman, K.J.; van der Walt, S.J.; Gommers, R.; Virtanen, P.; Cournapeau, D.; Wieser, E.; Taylor, J.; Berg, S.; Smith, N.J.; et al. Array programming with NumPy. Nature
**2020**, 585, 357–362. [Google Scholar] [CrossRef] - Howard, J.; Gugger, S. Fastai: A layered API for deep learning. Information
**2020**, 11, 108. [Google Scholar] [CrossRef] [Green Version] - Kaliyugarasan, S.K.; Lundervold, A.; Lundervold, A.S. Pulmonary Nodule Classification in Lung Cancer from 3D Thoracic CT Scans Using fastai and MONAI. Int. J. Interact. Multimed. Artif. Intell.
**2021**, 6, 83–89. [Google Scholar] [CrossRef] - Virtanen, P.; Gommers, R.; Oliphant, T.E.; Haberland, M.; Reddy, T.; Cournapeau, D.; Burovski, E.; Peterson, P.; Weckesser, W.; Bright, J.; et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat. Methods
**2020**, 17, 261–272. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Milletari, F.; Navab, N.; Ahmadi, S.A. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA, 25–28 October 2016; pp. 565–571. [Google Scholar]
- Wright, L. Ranger—A Synergistic Optimizer. 2019. Available online: https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer (accessed on 16 December 2021).
- Smith, L.N.; Topin, N. Super-convergence: Very fast training of neural networks using large learning rates. In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; International Society for Optics and Photonics: Bellingham, WA, USA, 2019; Volume 11006, p. 1100612. [Google Scholar]
- Cawley, G.C.; Talbot, N.L. On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res.
**2010**, 11, 2079–2107. [Google Scholar] - Lai, C.C.; Wang, H.K.; Wang, F.N.; Peng, Y.C.; Lin, T.P.; Peng, H.H.; Shen, S.H. Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks. Sensors
**2021**, 21, 2709. [Google Scholar] [CrossRef] - Hodneland, E.; Dybvik, J.A.; Wagner-Larsen, K.S.; Šoltészová, V.; Munthe-Kaas, A.Z.; Fasmer, K.E.; Krakstad, C.; Lundervold, A.; Lundervold, A.S.; Salvesen, Ø.; et al. Automated segmentation of endometrial cancer on MR images using deep learning. Sci. Rep.
**2021**, 11, 179. [Google Scholar] [CrossRef] - Kurata, Y.; Nishio, M.; Moribata, Y.; Kido, A.; Himoto, Y.; Otani, S.; Fujimoto, K.; Yakami, M.; Minamiguchi, S.; Mandai, M.; et al. Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network. Sci. Rep.
**2021**, 11, 14440. [Google Scholar] [CrossRef] - Trebeschi, S.; van Griethuysen, J.J.; Lambregts, D.M.; Lahaye, M.J.; Parmar, C.; Bakers, F.C.; Peters, N.H.; Beets-Tan, R.G.; Aerts, H.J. Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR. Sci. Rep.
**2017**, 7, 5301. [Google Scholar] [CrossRef] - Zhu, H.T.; Zhang, X.Y.; Shi, Y.J.; Li, X.T.; Sun, Y.S. Automatic segmentation of rectal tumor on diffusion-weighted images by deep learning with U-Net. J. Appl. Clin. Med. Phys.
**2021**, 22, 324–331. [Google Scholar] [CrossRef] - Liechti, M.R.; Muehlematter, U.J.; Schneider, A.F.; Eberli, D.; Rupp, N.J.; Hötker, A.M.; Donati, O.F.; Becker, A.S. Manual prostate cancer segmentation in MRI: Interreader agreement and volumetric correlation with transperineal template core needle biopsy. Eur. Radiol.
**2020**, 30, 4806–4815. [Google Scholar] [CrossRef] - Ji, W.; Yu, S.; Wu, J.; Ma, K.; Bian, C.; Bi, Q.; Li, J.; Liu, H.; Cheng, L.; Zheng, Y. Learning calibrated medical image segmentation via multi-rater agreement modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 12341–12351. [Google Scholar]
- Warfield, S.K.; Zou, K.H.; Wells, W.M. Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging
**2004**, 23, 903–921. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Roy, S.; Whitehead, T.D.; Quirk, J.D.; Salter, A.; Ademuyiwa, F.O.; Li, S.; An, H.; Shoghi, K.I. Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging. EBioMedicine
**2020**, 59, 102963. [Google Scholar] [CrossRef] [PubMed] - Bento, M.; Fantini, I.; Park, J.; Rittner, L.; Frayne, R. Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets. Front. Neuroinformatics
**2021**, 15, 805669. [Google Scholar] [CrossRef] [PubMed] - Yu, W.; Fang, B.; Liu, Y.; Gao, M.; Zheng, S.; Wang, Y. Liver vessels segmentation based on 3D residual U-NET. In Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 22–25 September 2019; pp. 250–254. [Google Scholar]
- Tashk, A.; Herp, J.; Nadimi, E. Fully automatic polyp detection based on a novel U-Net architecture and morphological post-process. In Proceedings of the 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO), Athens, Greece, 8–10 December 2019; pp. 37–41. [Google Scholar]
- Ngo, D.K.; Tran, M.T.; Kim, S.H.; Yang, H.J.; Lee, G.S. Multi-task learning for small brain tumor segmentation from MRI. Appl. Sci.
**2020**, 10, 7790. [Google Scholar] [CrossRef]

**Figure 1.**Graphical illustration of DL (deep learning) workflow and study setup. MRI data included T2-weighted images and diffusion weighted images (DWI), using high b-value images and apparent diffusion coefficient (ADC) maps at primary diagnostic work-up in 131 CC patients. (

**A**) In the train and validation cohort (n = 105), primary tumor was segmented by one of the two expert raters (R1: n = 58, R2: n = 47). (

**B**) The test cohort (n = 26), with primary tumor segmentations by both expert raters (R1 and R2), served as an unbiased test set for evaluating performance of the DL algorithm and inter-rater agreement. (

**C**) The train and validation cohort (n = 105) was used to train a 3D U-Net using 90/105 (86%) cases for training and 15/105 (14%) cases for validation. (

**D**) The trained network predicted raw tumor masks in the test data set (n = 26), identifying multiple regions in 23/26 cases. The object with the largest mean activation value was selected as primary tumor (yellow object). Other objects with lower mean activation values (red object) were removed from further analysis (indicated by a black cross). (

**E**) DL-derived tumor masks were compared with manually segmented masks from R1 and R2, using Dice score and Hausdorff distances. R1 = Rater 1, R2 = Rater 2.

**Figure 2.**A visualization of the activation map (colored regions) from the DL (deep learning) segmentation superimposed on T2-weighted MRI (grayscale colormap) for three orthogonal planes and using 3D volume rendering. The activation map was later transformed with a sigmoid function and then thresholded, resulting in a binary prediction map. Two objects were identified in this patient: The object positioned in the uterine cervix (yellow arrows) had the largest mean activation value and was thus automatically selected to represent primary tumor. The object positioned in the uterine cavity/body (red arrows) had lower mean activation value and was thus excluded.

**Figure 3.**Train and validation losses (left axis) and Dice scores (DSC) (right axis) depicted as a function of epoch number. The train loss is smoothly decreasing, indicating numerical stability of the algorithm. The Dice score reaches a plateau, suggesting an optimal epoch number of 55 (black, solid dots). This epoch number yields optimal training performance of the network while minimizing the risk of over-training. a.u. = arbitrary units.

**Figure 4.**(

**Left**): Histogram depicting number of objects in the prediction maps for the test cohort (n = 26) using a sigmoid-transformed activation map with a threshold of 0.5. In only 3/26 patients a single object was identified, whereas in 23/26 patients multiple mask objects were suggested. (

**Middle**): Surface rendering depicting two objects (in red and yellow) in one of the patients having two predicted objects (grey box in histogram). The surface colors red/yellow indicate corresponding low/high mean activation values for the two objects (a.u. = arbitrarily units). (

**Right**): In this patient with two suggested objects, the yellow mask with highest mean activation value was automatically selected as primary tumor.

**Figure 5.**Comparison of (

**a**) Median Dice coefficient (DSC) and (

**b**) Median Hausdorff distance (HD) for segmentations by DL-R1, DL-R2, and R1-R2. Agreement between R1-R2 is significantly better than between DL and R1/R2 in terms of DSC and HD (Wilcoxon rank sum test, p ≤ 0.01). The central line indicates the median, and the upper and edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers indicate the most extreme data points not considered to be outliers, while outliers are plotted individually using a ‘+’ symbol. R1 = rater 1; R2 = rater 2.

**Figure 6.**Bland–Altman plots comparing tumor volumes V [mL] from (

**a**) DL (deep learning) and R1, (

**b**) DL and R2 and (

**c**) R1 and R2. Red lines indicate mean difference of the estimate, and dashed lines represent lower and upper limits-of-agreement (LoA). Mean difference in estimated tumor volumes is low for all comparisons, indicating a high agreement in mean primary tumor volume by all methods. However, LoA is higher for DL-R1/R2 than for R1-R2, indicating a higher individual disagreement for tumor measurements by DL-R1/R2 than by R1-R2. R1 = rater 1; R2 = rater 2.

**Figure 7.**Tumor volume in relation to segmentation performance. (

**Left**): R1 tumor volume. (

**Middle**): R2 tumor volume. (

**Right**): Mean tumor volume for R1- and R2 masks. (

**Upper row**): Tumor volume against Dice coefficient (DSC). There is a weak but significant correlation between primary tumor volume and DSC for DL (deep learning)-R1/R2 (left and middle panel, p ≤ 0.046). R1-R2 DSC only tended to be associated with tumor volume (right panel, p = 0.12). (

**Lower row**): Plots of tumor volume against Hausdorff distance (HD). We found no significant correlation between primary tumor volume and HD for any of the associations DL-R1/R2 or R1-R2 (p≥ 0.24). Both rows: The same patients with (i) low DSC < 0.2 and (ii) a small tumor volume <50 mL (estimated tumor volume for this condition is either R1 (left), R2 (middle), or mean (R1, R2) tumor volume) are simultaneously marked in blue in upper and lower panels, suggesting that patients experiencing a low DSC are normally high in HD for ML-R1/R2 (left and middle panels, the same n = 6 patients were identified). For R1-R2, patients with low DSC also have low HD (right panel, n = 2 patients). R1 = rater 1; R2 = rater 2; V = tumor volume, {$\rho $, p} = Spearman rank correlation coefficient with associated p-value.

**Table 1.**Summary of MRI protocols used in the study cohort (n = 131). The MRI data were acquired using different protocols, field strength and vendors. T2-weighted and diffusion-weighted imaging (DWI) acquisition parameters are reported as median values. FA = flip angle; FOV = field of view; mm = millimeters; ms = milliseconds; NA = not available; n = Number of patients in each category in terms of field-strength and vendor; s = seconds; T2 = T2-weighted; T = Tesla; TE = echo time; TR = repetition time. * Available b-values are reported, but not all b-values were available after export of the image data from the scanner.

Parameter | Siemens 1.5T | GE 1.5T | Philips 1.5 | Siemens 3T | Philips 3T | |
---|---|---|---|---|---|---|

T2 | Pixel spacing [mm] (inplane) | (0.39, 0.39) | (0.35, 0.35) | (0.40, 0.40) | (0.52, 0.52) | (0.35, 0.35) |

Matrix (x, y) | (512, 512) | (512, 512) | (512, 512) | (384, 384) | (512, 512) | |

FOV [mm] (x, y) | (180, 180) | (180, 180) | (205, 205) | (200, 200) | (180, 180) | |

TR [ms] | 4790 | 3157 | 5362 | 4610 | 4074 | |

TE [ms] | 100 | 81 | 100 | 94 | 110 | |

FA [degrees] | 150 | 160 | 90 | 148 | 90 | |

Slice thickness [mm] | 3.00 | 3.00 | 3.00 | 3.00 | 2.50 | |

Number of averages | 2 | 2 | 6 | 2 | 2 | |

Interslice gap [mm] | 0.50 | 0.00 | 0.30 | 0.30 | 0.25 | |

Number of slices | 25 | 30 | 26 | 24 | 35 | |

DWI | Pixel spacing [mm] (x, y) | (1.56, 1.56) | (1.37, 1.37) | (1.46, 1.46) | (1.43, 1.43) | (0.80, 0.80) |

Matrix (x, y) | (144, 144) | (256, 256) | (256, 256) | (144, 144) | (352, 352) | |

FOV [mm] (x, y) | (250, 250) | (350, 350) | (375, 375) | (200, 200) | (280, 280) | |

TR [ms] | 3200 | 4000 | 1716.30 | 5640 | 3280 | |

TE [ms] | 82 | 52 | 69.18 | 63 | 85 | |

FA [degrees] | 90 | 90 | 90 | 180 | 90 | |

Slice thickness [mm] | 4.00 | 5.00 | 5.00 | 3.00 | 4.00 | |

Number of averages | 10 | 2 | 3 | 2 | 2 | |

Interslice gap [mm] | 0.60 | 0.50 | 1.00 | 0.40 | 0.40 | |

Number of slices | 22 | 25 | 30 | 25 | 33 | |

b-values [s/mm${}^{2}$] | [0/50, 800/1000] | NA * | [0, 1000] | [0/50, 800/1000] | NA * | |

N | Number of patients | 51 | 9 | 27 | 27 | 9 |

**Table 2.**Patient characteristics of the training/validation cohort (n = 105) and the test cohort (n = 26). The two patient cohorts have similar clinicopathological characteristics. ${}^{1}$ Mann–Whitney U test. ${}^{2}$ Pearson’s chi-square test. ${}^{3}$ Fisher exact test. ${}^{4}$ n = 97 for training/validation cohort and n = 25 for the test cohort. * Adenosquamous, neuroendocrine, and undifferentiated carcinomas; FIGO = International Federation of Gynecology and Obstetrics; IQR = Interquartile range; w/o = with and without.

Variable | Train (n = 90) and Validation (n = 15) Data | Test Data (n = 26) | p |
---|---|---|---|

Age (yrs.) | 0.73 ${}^{1}$ | ||

Median (IQR) | 48 (37–60) | 49 (41–59) | |

FIGO (2009) stage | 0.21 ${}^{2}$ | ||

I | 52 (49%) | 14 (54%) | |

II | 27 (26%) | 6 (23%) | |

III | 18 (17%) | 5 (19%) | |

IV | 8 (8%) | 1 (4%) | |

MRI-assessed maximum tumor size (cm) | 0.24 ${}^{1}$ | ||

Median (IQR) | 4.6 (3.0–5.6) | 3.9 (2.5–5.1) | |

Primary treatment | 0.21 ${}^{2}$ | ||

Surgery only | 26 (25%) | 9 (34%) | |

Surgery and adjuvant therapy | 63 (60%) | 15 (58%) | |

Primary radiotherapy w/o chemotherapy | 12 (11%) | 2 (8%) | |

Palliative treatment | 4 (4%) | 0 | |

Histologic subtype | 0.19 ${}^{2}$ | ||

Squamous cell carcinoma | 82 (78%) | 21 (81%) | |

Adenocarcinoma | 18 (17%) | 3 (11%) | |

Other * | 5 (5%) | 2 (8%) | |

Histologic grade ${}^{4}$ | 0.76 ${}^{3}$ | ||

Low/medium | 80 (82%) | 22 (88%) | |

High | 17 (18%) | 3 (12%) |

**Table 3.**Median (IQR = interquartile range) Dice score (DSC) and Hausdorff distance (HD) for tumor masks derived from DL (deep learning) segmentation compared to manual tumor segmentations by R1/R2. I: DL yields tumor masks with lower DSC and higher HD for DL-R1/R2 than that for R1-R2 (Wilcoxon rank sum, p ≤ 0.01 and p≤ 0.01, respectively). II: Performance metrics of tumor masks after adjusting for median R1-R2 disagreement. The adjusted values yield higher DSCs and lower HDs for DL-R1/R2 when using DSC = 1 and HD = 0 as reference values for R1-R2 (ref. values). * Statistically significant; ${}^{1}$ Statistical testing and difference in estimates do not change from I to II; R1 = rater 1; R2 = rater 2.

Measure | Median Value of Estimate (IQR) | Absolute Difference (p-Value) | ||||
---|---|---|---|---|---|---|

$\mathit{A}$. (DL, R1) | $\mathit{B}$. (DL, R2) | $\mathit{C}$. (R1, R2) | $|\mathit{A}-\mathit{C}|$(p) | $|\mathit{B}-\mathit{C}|$(p) | ||

I. Unadjusted | DSC | 0.60 (0.05, 0.78) | 0.58 (0.09, 0.76) | 0.78 (0.60, 0.83) | 0.19 (0.01 *) | 0.21 (0.005 *) |

HD [mm] | 29.2 (14.5, 57.5) | 30.2 (17.1, 55.9) | 14.6 (9.80, 30.7) | 14.6 (0.01 *) | 15.5 (0.003 *) | |

II. Adjusted for R1-R2 disagreement | DSC | 0.81 | 0.79 | 1 (ref.) | - ${}^{1}$ | - ${}^{1}$ |

HD [mm] | 3.73 | 9.10 | 0 (ref.) | - ${}^{1}$ | - ${}^{1}$ |

**Table 4.**Association between field strength, anisotropy T2 and DWI, field-of-view (FOV) T2 and DWI, and DSC using multiple linear regression. None for the MRI acquisition features had a statistical assocation to segmentation performance (p ≥ 0.33, multiple linear regression).

Estimate | SE | p | |
---|---|---|---|

(Intercept) | 0.08 | 0.36 | 0.82 |

Field strength | −0.06 | 0.15 | 0.71 |

Anisotropy T2 | 0.04 | 0.04 | 0.33 |

FOV T2 | 1.77 | 23.69 | 0.94 |

Anisotropy DWI | 0.04 | 0.07 | 0.54 |

FOV DWI | 4.10 | 6.11 | 0.51 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Hodneland, E.; Kaliyugarasan, S.; Wagner-Larsen, K.S.; Lura, N.; Andersen, E.; Bartsch, H.; Smit, N.; Halle, M.K.; Krakstad, C.; Lundervold, A.S.;
et al. Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer. *Cancers* **2022**, *14*, 2372.
https://doi.org/10.3390/cancers14102372

**AMA Style**

Hodneland E, Kaliyugarasan S, Wagner-Larsen KS, Lura N, Andersen E, Bartsch H, Smit N, Halle MK, Krakstad C, Lundervold AS,
et al. Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer. *Cancers*. 2022; 14(10):2372.
https://doi.org/10.3390/cancers14102372

**Chicago/Turabian Style**

Hodneland, Erlend, Satheshkumar Kaliyugarasan, Kari Strøno Wagner-Larsen, Njål Lura, Erling Andersen, Hauke Bartsch, Noeska Smit, Mari Kyllesø Halle, Camilla Krakstad, Alexander Selvikvåg Lundervold,
and et al. 2022. "Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer" *Cancers* 14, no. 10: 2372.
https://doi.org/10.3390/cancers14102372