Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges
Abstract
:1. Introduction
2. Background
2.1. What Is Deep Learning?
2.2. Deep Learning in Oncology
2.3. Quantitative Imaging for Cancer Diagnosis, Characterization and Assessment of Treatment Response
2.4. Radiotherapy Treatment (RT) Planning and Optimization
2.5. Automatic Image Segmentation
Evaluating the Quality and Success of Segmentation
3. Literature Review
- non-DL segmentation techniques;
- segmentation applied to sites other than the pelvis;
- no training/validation of methods on real patient data;
- image modalities used other than CT and MRI;
- full articles published in languages other than English;
- no clinical application focus or published outcome
3.1. Bladder Cancer
3.2. Cervical Cancer
3.3. Prostate Cancer
3.4. Rectal Cancer
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Image Modality | Deep Learning Strategy | DL Network Dimension | Number of Patients (Train/Test) | Segmentation Evaluation Metrics | Year | Reference |
---|---|---|---|---|---|---|
(MR Acquisition Mode) | ||||||
Bladder Cancer | ||||||
CT | U-Net | 2D/3D | 81/92 | Bladder (IoU: 0.85/0.82) | 2019 | [88] |
CT | CNN + FCN (CRF-RNN) | 3D | 100/24 | Bladder (DSC: 0.92) | 2018 | [94] |
CT | CNN | 2D | 62 leave-one-out cross validation | Bladder Tumor (area under the ROC curve (AUC): 0.73) | 2016 | [87] |
CT | CNN | 2D | 81/92 | Bladder (IoU: 0.76) | 2016 | [93] |
T2W (2D), DW (2D) MRI | AE + modified residual network (BW-Net) | 2D | 144/25 | Bladder Wall (DSC: 0.85) | 2020 | [96] |
T2W MRI (3D) | U-Net with progressive dilated convolutions (U-Net Progressive) | 2D | 40/15 | Bladder Tumor (DSC: 0.68), Outer Wall (DSC: 0.83), Inner Wall (DSC: 0.98) | 2018 | [95] |
Cervical Cancer | ||||||
CT | U-Net with context aggregation blocks (CabUNet) | 2D | 77/14 | Bladder (DSC: 0.90), Bone Marrow (DSC: 0.85), L Fem. Head (DSC: 0.90), R Fem. Head (DSC: 0.90), Rectum (DSC: 0.79), Small Intestine (DSC: 0.83), Spinal Cord (DSC: 0.82) | 2020 | [97] |
CT | Dual path U-Net (DpnUNet) | 2.5D | 210 five-fold cross validation | CTV (DSC: 0.86), Bladder (DSC: 0.91), Bone Marrow (DSC: 0.85), L Fem. Head (DSC: 0.90), R Fem. Head (DSC: 0.90), Rectum (DSC: 0.82), Bowel Bag (DSC: 0.85), Spinal Cord (DSC: 0.82) | 2020 | [98] |
CT | U-Net | 3D | 100/25 | CTV (DSC: 0.86), Bladder (DSC: 0.88), Rectum (DSC: 0.81), L Fem. Head (DSC: 0.88), R Fem. Head (DSC: 0.88), Small Intestine (DSC: 0.86) | 2020 | [99] |
CT | U-Net with residual connection, dilated convolution and deep supervision (DSD-UNet) | 3D | 73/18 | High-risk CTV (DSC: 0.82, IOU: 0.72), Bladder (DSC: 0.86, IOU: 0.77), Rectum (DSC: 0.82, IOU: 0.71), Small Intestine (DSC: 0.80, IOU: 0.69), Sigmoid (DSC: 0.64, IOU: 0.52) | 2020 | [100] |
CT | V-Net | 3D | 2464/140 (+30 external test patients) | Primary CTV (UteroCervix) (DSC: 0.85), Nodal CTV (DSC: 0.86), PAN CTV (DSC: 0.76), Bladder (DSC: 0.89), Rectum (DSC: 0.81), Spinal Cord (DSC: 0.90), L Femur (DSC: 0.94), R Femur (DSC: 0.93), L Kidney (DSC: 0.94), R Kidney (DSC: 0.95), Pelvic Bone (DSC: 0.93), Sacrum (DSC: 0.91), L4 Vertebral Body (DSC: 0.91), L5 Vertebral Body (DSC: 0.90) | 2020 | [101] |
MRI (unspecified) | Mask R-CNN | 2D | 5 (646 images split 9:1 for training and testing) | GTV + Cervix (DSC: 0.84), Uterus (DSC: 0.92), Sigmoid (DSC: 0.89), Bladder (DSC: 0.90), Rectum (DSC: 0.89), Parametrium (DSC: 0.66), Vagina (DSC: 0.71), Mesorectum (DSC: 0.68), Femur (DSC: 0.81) | 2019 | [102] |
DW MRI (2D) | U-Net | 2D | 144/25 | Cervical Tumor (DSC: 0.82) | 2019 | [17] |
Prostate Cancer | ||||||
CT | U-Net (External commercial software) | 2D | 328/20 | Prostate (DSC: 0.79), Bladder (DSC: 0.97), Rectum (DSC: 0.78), Fem. Head (DSC: 0.91), Seminal Vesicles (DSC: 0.64) | 2020 | [103] |
CT | U-Net | 3D | 900/30 | Prostate (DSC: 0.82), Bladder (DSC: 0.93), Rectum (DSC: 0.84), L Fem. Head (DSC: 0.68), R Fem. Head (DSC: 0.69), Lymph Nodes (DSC: 0.80), Seminal Vesicles (DSC: 0.72) | 2020 | [104] |
CT | High-resolution multi-scale encoder-decoder network (HMEDN) | 2D | 180/100 | Prostate (DSC: 0.88), Bladder (DSC: 0.94), Rectum (DSC: 0.87) | 2019 | [105] |
CT/ Synthetic T2W MRI | CT-to-MR synthesis + Deep Attention U-Net (DAUNet) | 3D | 112/28 five-fold cross validation | Prostate (DSC: 0.87), Bladder (DSC: 0.95), Rectum (DSC: 0.89) | 2019 | [106] |
CT | Modified U-Net | 3D | 313 five-fold cross validation | Prostate: (DSC: 0.89), Bladder: (DSC: 0.94), Rectum: (DSC: 0.89) | 2019 | [107] |
CT | Deep Neural Network (DNN) | 3D | 771/140 | Prostate (DSC: 0.88) | 2019 | [108] |
CT | Deeply-supervised attention-enabled boosted convolutional neural network (DAB-CNN) | 3D | 80/20 | Prostate (DSC: 0.90), Bladder (DSC: 0.93), Rectum (DSC: 0.83), Penile bulb (DSC: 0.72) | 2019 | [109] |
CT | Distinctive curve guided fully convolutional network (FCN) | 2D | 313 five-fold cross validation | Prostate (DSC: 0.89), Bladder (DSC: 0.94), Rectum (DSC: 0.89) | 2019 | [110] |
CT | U-Net | 2D | 60/25 | Prostate: (DSC: 0.88), Bladder: DSC: 0.95), Rectum: (DSC: 0.92) | 2018 | [111] |
CT | 2D U-Net + 3D U-Net with aggregated residual networks (ResNeXt) | 2D/3D | 108/28 four-fold cross validation | Prostate (DSC: 0.90), Bladder (DSC: 0.95), Rectum (DSC: 0.84), L Fem. Head (DSC: 0.96), R Fem. Head (DSC: 0.95) | 2018 | [112] |
CT | CNN + multi-atlas fusion | 2D | 92 five-fold cross validation | Prostate (DSC: 0.86) | 2017 | [31] |
CT | FCN (based on LeNet) | 2D | 22 two-fold cross validation | Prostate (DSC: 0.89) | 2017 | [113] |
T2W MRI (2D) | Adversarial pyramid anisotropic convolutional deep neural network (APA-Net) | 3D | 110 three-fold cross validation | Whole Prostate Gland (DSC: 0.90) | 2020 | [114] |
T2W MRI (2D/3D) | DeeplabV3+ | 2D | 40 | Prostate Central Gland (DSC: 0.81), Peripheral Zone (DSC: 0.70) | 2020 | [115] |
T2W (2D), DW (2D) MRI | Conditional GAN (cGAN)/Cycle-consistent GAN (Cycle-GAN) | 2D | 40/50 | Whole Prostate Gland (DSC: 0.75) | 2020 | [116] |
T2W (2D), DW (2D) MRI | Mask R-CNN | 2D | 54/16 (+12 external test patients) | Whole Prostate Gland (DSC: 0.86), Prostate Tumor (DSC: 0.56) | 2020 | [117] |
T2W MRI (2D) | Boundary-weighted domain adaptive neural network (BOWDA-Net) | 3D | 40/146 | Whole Prostate Gland (DSC: 0.91) Prostate Base (DSC: 0.89) Prostate Apex (DSC: 0.89) | 2020 | [118] |
T2W MRI (2D) | Graph convolutional network (GCN) | 2D | 140 five-fold cross validation | Whole Prostate Gland (DSC: 0.93) | 2020 | [119] |
T2W MRI (2D) | Dense U-Net | 2D | 141/47 four-fold cross validation | Whole Prostate Gland (DSC: 0.92), Central Gland (DSC: 0.89), Peripheral Zone (DSC: 0.78) | 2020 | [120] |
T2W MRI (2D) | U-Net/Pix2pix | 2D | 40 four-fold cross validation | Prostate Central Gland (DSC: 0.86–0.88), Peripheral Zone (DSC: 0.90–0.83) | 2020 | [121] |
T1W (3D), T2W (unspecified) MRI | Multi-scale DeepMedic | 3D | 97/53 three-fold cross validation | Bladder (DSC: 0.96), Rectum (DSC: 0.88), L femur (DSC: 0.97), R femur (DSC: 0.97) | 2020 | [122] |
T2W MRI (2D) | Cascaded dual attention network (CDA-Net) | 3D | 40/109 | Whole Prostate Gland (DSC: 0.92) | 2020 | [123] |
T2W MRI (2D) | Encoder-Decoder structure with dense dilated spatial pyramid pooling (DDSPP) | 2D | 150 | Whole Prostate Gland (DSC: 0.95) | 2019 | [124] |
T2W (2D), DW (2D) MRI | Mask R-CNN | 2D | 36 (split 7:2:1 for training, validation and testing) | Whole Prostate Gland (IoU: 0.84), Prostate Tumor (IoU: 0.40), Central Gland (IoU: 0.78), Peripheral Zone (IoU: 0.51) | 2019 | [125] |
T2W (2D), DW (2D) MRI | U-Net | 2D | 100/125 | Whole Prostate Gland (DSC: 0.84), Central Gland (DSC: 0.78), Peripheral Zone (DSC: 0.69) | 2019 | [126] |
T2W MRI (2D) | FCN with feature pyramid attention | 2D | 250/63 (+46 external test patients) | Prostate Transition Zone (DSC: 0.79), Peripheral zone (DSC: 0.74) | 2019 | [127] |
T2W MRI (3D) | Spatially-varying stochastic residual adversarial network (STRAINet) | 3D | 50 five-fold cross validation | Whole Prostate Gland (DSC: 0.91), Bladder (DSC: 0.97), Rectum (DSC: 0.91) | 2019 | [128] |
T2W MRI (2D) | U-Net with “combo loss” | 3D | 700/258 | Whole Prostate Gland (DSC: 0.91) | 2019 | [129] |
T2W MRI (unspecified) | DeepLabV3+ | 2D | 40/50 | CTV (DSC: 0.83), Bladder (DSC: 0.93), Rectum (DSC: 0.82), Penile Bulb (DSC: 0.74), Urethra (DSC: 0.69), Rectal Spacer (DSC: 0.81) | 2019 | [130] |
T2W MRI (2D) | V-Net + variational methods | 3D | 85 | Whole Prostate Gland (DSC: 0.64) | 2019 | [131] |
T2W MRI (2D) | Propagation Deep Neural Network (P-DNN) | 2D | 50/30 | Whole Prostate Gland: (DSC: 0.84) | 2019 | [132] |
T2W (2D), DW (2D) MRI | Cascaded U-Net | 2D | 76/51 | Whole Prostate Gland (DSC: 0.92), Peripheral zone (DSC: 0.79) | 2019 | [133] |
T2W MRI (3D) | Multi-view CNN | 2D | 19 leave-one-out cross validation | Prostate Tumor (DSC: 0.92, IoU: 0.67), Prostate Central Gland (IoU: 0.65), Peripheral Zone (IoU: 0.59) | 2019 | [134] |
T2W MRI (2D) | Investigative CNN study (U-Net, V-Net, HighRes3dNet, HolisticNet, Dense V-Net, Adapted U-Net) | 3D | 173/59 | Whole Prostate Gland (DSC: 0.87) | 2019 | [135] |
T2W MRI (2D) | Z-Net | 2D | 45/30 | Whole Prostate Gland (DSC: 0.90) | 2019 | [136] |
T2W MRI (3D) | FCN | 3D | 60/10 | Whole Prostate Gland (DSC: 0.89), Bladder (DSC: 0.95), Rectum (DSC: 0.88) | 2018 | [137] |
T2W MRI (2D) | SegNet | 2D | 16/5 (+19 external test patients) | Whole Prostate Gland (DSC: 0.75) | 2018 | [138] |
T2W MRI (2D) | CNN + Boundary Detection | 3D | 50 five-fold cross validation | Whole Prostate Gland (DSC: 0.90) | 2018 | [139] |
Dynamic Contrast-Enhanced (DCE) MRI (3D) | U-Net + Long-Short-Term Memory (LSTM) | 3D | (15/2) three-fold cross validation | Whole Prostate Gland (DSC: 0.86) | 2018 | [140] |
T2W MRI (2D) | FCN | 2D | 50/30 | Whole Prostate Gland (DSC: 0.87) | 2018 | [141] |
T2W MRI (2D) | CNN | 2D | 20 | Whole Prostate Gland (DSC: 0.85) | 2018 | [30] |
T2W MRI (2D) | CNN (PSNet) | 3D | 112/28 five-fold cross validation | Whole Prostate Gland (DSC: 0.85) | 2018 | [29] |
T2W (2D), DW (2D) MRI | Deep dense multi-path CNN | 3D | 100/50 (+30 external test patients) | Whole Prostate Gland (DSC: 0.95) | 2018 | [142] |
T2W MRI (2D) | U-Net | 3D | 26 | Whole Prostate Gland (DSC: 0.88) | 2018 | [143] |
T2W MRI (2D) | Deeply-supervised CNN | 2D | 77/4 | Whole Prostate Gland (DSC: 0.89) | 2017 | [144] |
T2W (2D), DW (2D) MRI | Auto-Encoder | 2D | 21 leave-one-out cross validation | Prostate Tumor (section-based evaluation (SBE): 0.89, sensitivity: 91%, specificity: 88%) | 2017 | [145] |
T2W MRI (2D) | Holistically-nested FCN | 2D | 250 five-fold cross validation | Whole Prostate Gland (DSC: 0.89, IoU: 0.81) | 2017 | [146] |
DW MRI (2D) | Modified U-Net with inception blocks | 2D | 141 four-fold cross validation | Whole Prostate Gland (DSC: 0.93), Transition Zone (DSC: 0.88) | 2017 | [147] |
T2W MRI (2D) | ConvNet with mixed residual connections | 3D | 50/30 | Whole Prostate Gland (DSC: 0.87) | 2017 | [148] |
T2W MRI (2D) | Stacked Sparse AE (SSAE) + Sparse patch matching | 2D | 66 two-fold cross validation | Whole Prostate Gland (DSC: 0.87) | 2016 | [149] |
T2W MRI (2D) | V-Net | 3D | 50/30 | Whole Prostate Gland (DSC: 0.87) | 2016 | [79] |
T2W MRI (unspecified) | Stacked independent subspace analysis (ISA) | 2D | 30 leave-one-out cross validation | Whole Prostate Gland (DSC: 0.86) | 2013 | [150] |
Rectal Cancer | ||||||
CT | DeepLabV3+ | 2D | 98/63 | CTV (DSC: 0.88), Bladder (DSC: 0.90), Small Intestine (DSC: 0.76), L Fem. Head (DSC: 0.93), R Fem. Head (DSC: 0.93) | 2020 | [32] |
CT/ T2W MRI (2D) | CNN with cascaded atrous convolution (CAC) and spatial pyramid pooling module (SPP) | 2D | 100/70 five-fold cross validation | Rectal Tumor (DSC: 0.78) CTV (DSC: 0.85) | 2018 | [151] |
CT | Dilated CNN (transfer learning from VGG-16) | 2D | 218/60 | CTV (DSC: 0.87), Bladder (DSC: 0.93), L Fem. Head (DSC: 0.92), R Fem. Head (DSC: 0.92), Intestine (DSC: 0.65), Colon (DSC: 0.62) | 2017 | [152] |
T2W (2D), DW (2D) MRI | Mask R-CNN | 2D | 293/31 (+50 external test patients) | Lymph Nodes (DSC: 0.81) | 2020 | [153] |
T2W MRI (2D) | CNN (transfer learning from ResNet50) | 2D | 461/107 | Rectal Tumor (DSC: 0.82) | 2019 | [154] |
T2W MRI (3D) | U-Net | 2D | 93 ten-foldcross validation | Rectal GTV (DSC: 0.74, IoU: 0.60) | 2018 | [155] |
T2W MRI (2D) | FCN (transfer learning from VGG-16) | 2D | 410/102 | Rectal Tumor (DSC: 0.84) | 2018 | [28] |
T2W MRI (2D) | Hybrid loss FCN (HL-FCN) | 3D | 64 four-fold cross validation | Rectal Tumor (DSC: 0.72) | 2018 | [156] |
T2W (unspecified), DW (2D) MRI | CNN | 2D | 70/70 | Rectal Tumor (DSC: 0.69) | 2017 | [157] |
Dataset | Image Modality (MRI Acquisition Mode) | Number of Patients | Ground-Truth Contours | URL | Studies |
---|---|---|---|---|---|
PROMISE12 [163] | T2W MRI (2D) | 80 | Whole Prostate Gland | https://promise12.grand-challenge.org/ [Accessed 21 October 2021] | [29,79,114,116,118,119,123,124,128,130,131,132,133,136,141,142,143,147,148] |
I2CVB [167] | T2W (2D/3D), | 40 | Whole Prostate Gland, Peripheral Zone, Central Gland, Prostate Tumor | https://i2cvb.github.io/ [Accessed 21 October 2021] | [115,125,134,138,140,168] |
DW (2D), | |||||
DCE (3D), | |||||
MRSI (3D) MRI | |||||
BWH [169] | T1W (2D/3D), | 230 | Whole Prostate Gland | https://prostatemrimagedatabase.com/ [Accessed 21 October 2021] | [118,131] |
T2W (2D) MRI | |||||
ASPS13 [164] | T1W (2D), | 156 | Whole Prostate Gland, Peripheral Zone | https://wiki.cancerimagingarchive.net/display/Public/NCI-ISBI+2013+Challenge+-+Automated+Segmentation+of+Prostate+Structures [Accessed 21 October 2021] | [29,114,123,124] |
T2W (2D), | |||||
DCE (3D) MRI | |||||
PROSTATEx [165] | T2W (2D), | 330 (malignant lesions: 76, benign lesions: 245) | Prostate Tumor | https://prostatex.grand-challenge.org/ [Accessed 21 October 2021] | [120,125,127,129] |
DW (2D), | |||||
PDW (3D), | |||||
DCE (3D) MRI |
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Kalantar, R.; Lin, G.; Winfield, J.M.; Messiou, C.; Lalondrelle, S.; Blackledge, M.D.; Koh, D.-M. Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges. Diagnostics 2021, 11, 1964. https://doi.org/10.3390/diagnostics11111964
Kalantar R, Lin G, Winfield JM, Messiou C, Lalondrelle S, Blackledge MD, Koh D-M. Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges. Diagnostics. 2021; 11(11):1964. https://doi.org/10.3390/diagnostics11111964
Chicago/Turabian StyleKalantar, Reza, Gigin Lin, Jessica M. Winfield, Christina Messiou, Susan Lalondrelle, Matthew D. Blackledge, and Dow-Mu Koh. 2021. "Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges" Diagnostics 11, no. 11: 1964. https://doi.org/10.3390/diagnostics11111964