Boosting‐based cascaded convolutional neural networks for the segmentation of CT organs‐at‐risk in nasopharyngeal carcinoma

Zhong, Tao; Huang, Xia; Tang, Fan; Liang, Shujun; Deng, Xiaogang

doi:10.1002/mp.13825

Cited by 41 publications

(38 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Parotid glands DC (%) 92 AE 4, 37 91 AE 2, 75 88 AE 2, 46 91 m;f (N), 45 88, 53 87 AE 3(N), 60 87 AE 4 (N), 24 87, 64 86 AE 2 (N), 40 86 AE 3, 48 86 AE 4, 24 86 AE 5 (N), 31 86 AE 5, 42 86 AE 5, 40 86 AE 7, 93 91 m;f , 72 85 AE 2, 83 85 AE 3, 26 85 AE 4, 91 85 AE 4, 30 85 AE 5, 47 91 m;f (DL), 29 85, 60 84 AE 3, 34 84 AE 3 (N), 55 84 AE 4 (•), 60 84 AE 7 (N,IM), 66 84, 76 91 m;f , 22 91 m;f , 23 83 AE 2, 50 83 AE 3, 58 83 AE 5 (•), 36 83 AE 5, 86 83 AE 6, 36 83 AE 6 (N), 56 91 m;f , 95 91 m;f , 81 AE 4 (N), 70 81 AE 5, 28 81 AE 8 (N), 49 81 AE 8, 27 81 (N), 54 91 m;f , 52 91 m;f (ABAS), 29 79 (MR), 68 91 m;f , 77 79, 87 79, 57 77 AE 6, 65 91 m;f (N), 69 91 m;f (N), 35 76 AE 6, 63 76 (CT), 68 91 m;f , 35 75, 51 72 AE 10, …”

Section: Resultsmentioning

confidence: 99%

“…After reviewing their abstracts, 49 were considered to be relevant and were further supplemented with selected publications from their list of references. In total, we collected 75 publications 22–96 focused on RT planning and three studies focused on hyperthermia therapy planning 97–99 from 2008 to date (Fig. 1), along with three review papers related to auto‐segmentation in the H&N region 19–21 .…”

Section: Resultsmentioning

confidence: 99%

“…Parotid glands [22][23][24][26][27][28][29][30][31][32][34][35][36][37]40,42,[45][46][47][48][49][50][51][52][53][54][55][56][57][58]60,[63][64][65][66][68][69][70]72,73,[75][76][77][78]80,[82][83][84]86,87,90,91,93,95…”

Section: Organ At Riskunclassified

“…Maximum distance to agreement 27,77 max b2@B dðb; @AÞ HD 95 95-percentile Hausdorff distance 22,23,[29][30][31]35,[37][38][39][40]46,49,55,58,66,69…”

Section: Dta Maxmentioning

confidence: 99%

“…AE 0.6,36 1.7 AE 0.7 (•),36 91 m;f ,73 5.1 AE 1.1, 48 91 m;f ,76 10.7, 53 91 m;f ,84 12.1 AE 3.9, 58 91 m;f , 52 91 m;f (N,IM),66 14.2 AE 6.6 (N)70 ; DTA91 m;f : 6.8 AE 2.5, 27 91 m;f (N),35 91 m;f , 35 91 m;f 77 ; HD91 m;f : 91 m;f (N),69 2.6 AE 1.4,40 2.7 AE 1.1 (N),31 3.2 AE 0.6,37 3.8 AE 1.1 (N),40 4.0 AE 2.2(N),55 91 m;f , 22 4.6 AE 1.2, 58 5.0 AE 2.4 (N,IM), 66 91 m;f , 23 5.2 AE 1.8 (N), 49 91 m;f (DL), 29 6.6 AE 3.3, 30 91 m;f , 35 91 m;f (N), 35 91 m;f (ABAS), 29 9.3 AE 3.3…”

mentioning

confidence: 99%

See 4 more Smart Citations

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

et al. 2020

View full text Add to dashboard Cite

Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor cells while sparing the healthy tissues. For this purpose, target volumes and OARs have to be delineated and segmented from medical images. As manual delineation is a tedious and time-consuming task subjected to intra/interobserver variability, computerized auto-segmentation has been developed as an alternative. The field of medical imaging and RT planning has experienced an increased interest in the past decade, with new emerging trends that shifted the field of H&N OAR auto-segmentation from atlas-based to deep learning-based approaches. In this review, we systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date, and provided critical discussions and recommendations from various perspectives: image modalityboth computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OARthe spinal cord, brainstem, and major salivary glands are the most studied OARs, but additional experiments should be conducted for several less studied soft tissue structures; image databaseseveral image databases with the corresponding ground truth are currently available for methodology evaluation, but should be augmented with data from multiple observers and multiple institutions; methodologycurrent methods have shifted from atlas-based to deep learning autosegmentation, which is expected to become even more sophisticated; ground truthdelineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metricsthe Dice coefficient as the standard volumetric overlap metrics should be accompanied with at least one distance metrics, and combined with clinical acceptability scores and risk assessments; segmentation performancethe best performing methods achieve clinically acceptable auto-segmentation for several OARs, however, the dosimetric impact should be also studied to provide clinically relevant endpoints for RT planning.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Organ At Riskunclassified

“…Maximum distance to agreement 27,77 max b2@B dðb; @AÞ HD 95 95-percentile Hausdorff distance 22,23,[29][30][31]35,[37][38][39][40]46,49,55,58,66,69…”

Section: Dta Maxmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

et al. 2020

View full text Add to dashboard Cite

show abstract

Comparative parotid gland segmentation by using ResNet‐18 and MobileNetV2 based DeepLab v3+ architectures from magnetic resonance images

Sünnetci

Kaba

Çeliker

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary Nowadays, artificial intelligence‐based medicine plays an important role in determining correlations not comprehensible to humans. In addition, the segmentation of organs at risk is a tedious and time‐consuming procedure. Segmentation of these organs or tissues is widely used in early diagnosis, treatment planning, and diagnosis. In this study, we trained semantic segmentation networks to segment healthy parotid glands using deep learning. The dataset we used in the study was obtained from Recep Tayyip Erdogan University Training and Research Hospital, and there were 72 T2‐weighted magnetic resonance (MR) images in this dataset. After these images were manually segmented by experts, masks of these images were obtained according to them and all images were cropped. Afterward, these cropped images and masks were rotated 45°, 120°, and 210°, quadrupling the number of images. We trained ResNet‐18/MobileNetV2‐based DeepLab v3+ without augmentation and ResNet‐18/MobileNetV2‐based DeepLab v3+ with augmentation using these datasets. Here, we set the training set and testing set sizes for all architectures to be 80% and 20%, respectively. We designed two different graphical user interface (GUI) applications so that users can easily segment their parotid glands by utilizing all of these deep learning‐based semantic segmentation networks. From the results, mean‐weighted dice values of MobileNetV2‐based DeepLab v3+ without augmentation and ResNet‐18‐based DeepLab v3+ with augmentation were equal to 0.90845–0.93931 and 0.93237–0.96960, respectively. We also noted that the sensitivity (%), specificity (%), F1 score (%) values of these models were equal to 83.21, 96.65, 85.04 and 89.81, 97.84, 87.80, respectively. As a result, these designed models were found to be clinically successful, and the user‐friendly GUI applications of these proposed systems can be used by clinicians. This study is competitive as it uses MR images, can automatically segment both parotid glands, the results are meaningful according to the literature and have software application.

show abstract

Automatic segmentation of organs‐at‐risks of nasopharynx cancer and lung cancer by cross‐layer attention fusion network with TELD‐Loss

et al. 2021

View full text Add to dashboard Cite

Purpose: Radiotherapy is one of the main treatments of nasopharyngeal cancer (NPC) and lung cancer. Accurate segmentation of organs at risks (OARs) in CT images is a key step in radiotherapy planning for NPC and lung cancer. However, the segmentation of OARs is influenced by the highly imbalanced size of organs, which often results in very poor segmentation results for small and difficult-to-segment organs. In addition, the complex morphological changes and fuzzy boundaries of OARs also pose great challenges to the segmentation task. In this paper, we propose a cross-layer attention fusion network (CLAF-CNN) to solve the problem of accurately segmenting OARs. Methods: In CLAF-CNN, we integrate the spatial attention maps of the adjacent spatial attention modules to make the segmentation targets more accurately focused, so that the network can capture more target-related features. In this way, the spatial attention modules in the network can be learned and optimized together. In addition, we introduce a new Top-K exponential logarithmic Dice loss (TELD-Loss) to solve the imbalance problem in OAR segmentation. The TELD-Loss further introduces a Top-K optimization mechanism based on Dice loss and exponential logarithmic loss, which makes the network pay more attention to small organs and difficult-to-segment organs, so as to enhance the overall performance of the segmentation model. Results: We validated our framework on the OAR segmentation datasets of the head and neck and lung CT images in the StructSeg 2019 challenge. Experiments show that the CLAF-CNN outperforms the state-of -the-art attentionbased segmentation methods in the OAR segmentation task with average Dice coefficient of 79.65% for head and neck OARs and 88.39% for lung OARs. Conclusions: This work provides a new network named CLAF-CNN which contains cross-layer spatial attention map fusion architecture and TELD-Loss for OAR segmentation. Results demonstrated that the proposed method could obtain accurate segmentation results for OARs, which has a potential of improving the efficiency of radiotherapy planning for nasopharynx cancer and lung cancer.

show abstract

Boosting‐based cascaded convolutional neural networks for the segmentation of CT organs‐at‐risk in nasopharyngeal carcinoma

Cited by 41 publications

References 38 publications

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

Comparative parotid gland segmentation by using ResNet‐18 and MobileNetV2 based DeepLab v3+ architectures from magnetic resonance images

Automatic segmentation of organs‐at‐risks of nasopharynx cancer and lung cancer by cross‐layer attention fusion network with TELD‐Loss

Contact Info

Product

Resources

About