Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples

Ju, Zhongjian; Wu, Qingnan; Yang, Wei; Gu, Shanshan; Guo, Wen; Wang, Jin‐Yuan; Ge, Ruigang; Hong, Quan; Liu, Jie; Qu, Baolin

doi:10.1080/0284186x.2020.1775290

Cited by 15 publications

(15 citation statements)

References 20 publications

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“…In the last few years, several semi‐automated or automated segmentation approaches have been proposed to support ROs in reducing contouring time. 1 , 2 , 3 , 4 , 5 Among automated segmentation approaches, only those based on atlases 6 , 7 , 8 , 9 , 10 or on artificial intelligence (AI) 6 , 11 , 12 , 13 , 14 , 15 can aim to fully automate contouring processes. Atlas‐based methods are implemented by several vendors as treatment planning system options or stand‐alone software modules.…”

Section: Introductionmentioning

confidence: 99%

Clinical validation of an automatic atlas‐based segmentation tool for male pelvis CT images

Casati

Piffer

Calusi

et al. 2022

J Applied Clin Med Phys

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This retrospective work aims to evaluate the possible impact on intraand inter-observer variability, contouring time, and contour accuracy of introducing a pelvis computed tomography (CT) auto-segmentation tool in radiotherapy planning workflow. Methods: Tests were carried out on five structures (bladder, rectum, pelvic lymph-nodes, and femoral heads) of six previously treated subjects, enrolling five radiation oncologists (ROs) to manually re-contour and edit auto-contours generated with a male pelvis CT atlas created with the commercial software MIM MAESTRO. The ROs first delineated manual contours (M). Then they modified the auto-contours, producing automatic-modified (AM) contours. The procedure was repeated to evaluate intra-observer variability, producing M1, M2, AM1, and AM2 contour sets (each comprising 5 structures × 6 test patients × 5 ROs = 150 contours), for a total of 600 contours. Potential time savings was evaluated by comparing contouring and editing times. Structure contours were compared to a reference standard by means of Dice similarity coefficient (DSC) and mean distance to agreement (MDA), to assess intra-and inter-observer variability. To exclude any automation bias, ROs evaluated both M and AM sets as "clinically acceptable" or "to be corrected" in a blind test. Results: Comparing AM to M sets, a significant reduction of both inter-observer variability (p < 0.001) and contouring time (-45% whole pelvis, p < 0.001) was obtained. Intra-observer variability reduction was significant only for bladder and femoral heads (p < 0.001). The statistical test showed no significant bias. Conclusion: Our atlas-based workflow proved to be effective for clinical practice as it can improve contour reproducibility and generate time savings. Based on these findings, institutions are encouraged to implement their autosegmentation method.

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Section: Introductionmentioning

confidence: 99%

Clinical validation of an automatic atlas‐based segmentation tool for male pelvis CT images

Casati

Piffer

Calusi

et al. 2022

J Applied Clin Med Phys

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“…Second, compared to studies conducted by Ju et al. 23 and Kazemifar et al., 24 who achieved a DSC around 0.85 delineating the small bowel using other models, the delineation standards of the small bowel of their studies are different from our standard. Figure 4 shows a comparison of our delineation standards of the small bowel to the standard used in the study of Ju et al.…”

Section: Discussionmentioning

confidence: 58%

“…and Kazemifar et al., 24 who achieved a DSC around 0.85 delineating the small bowel using other models, the delineation standards of the small bowel of their studies are different from our standard. Figure 4 shows a comparison of our delineation standards of the small bowel to the standard used in the study of Ju et al 23 . We did not include the soft tissues and other structures covered by the entire small bowel during contouring and only included a single small bowel model, which made our intestinal structure more difficult to be learned by deep learning networks.…”

Section: Discussionmentioning

confidence: 99%

Three‐dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images

Ding

Chen

Wang

et al. 2022

J Applied Clin Med Phys

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Purpose Radiation therapy is an essential treatment modality for cervical cancer, while accurate and efficient segmentation methods are needed to improve the workflow. In this study, a three‐dimensional V‐net model is proposed to automatically segment clinical target volume (CTV) and organs at risk (OARs), and to provide prospective guidance for low lose area. Material and methods A total of 130 CT datasets were included. Ninety cases were randomly selected as the training data, with 10 cases used as the validation data, and the remaining 30 cases as testing data. The V‐net model was implemented with Tensorflow package to segment the CTV and OARs, as well as regions of 5 Gy, 10 Gy, 15 Gy, and 20 Gy isodose lines covered. The auto‐segmentation by V‐net was compared to auto‐segmentation by U‐net. Four representative parameters were calculated to evaluate the accuracy of the delineation, including Dice similarity coefficients (DSC), Jaccard index (JI), average surface distance (ASD), and Hausdorff distance (HD). Results The V‐net and U‐net achieved the average DSC value for CTV of 0.85 and 0.83, average JI values of 0.77 and 0.75, average ASD values of 2.58 and 2.26, average HD of 11.2 and 10.08, respectively. As for the OARs, the performance of the V‐net model in the colon was significantly better than the U‐net model (p = 0.046), and the performance in the kidney, bladder, femoral head, and pelvic bones were comparable to the U‐net model. For prediction of low‐dose areas, the average DSC of the patients’ 5 Gy dose area in the test set were 0.88 and 0.83, for V‐net and U‐net, respectively. Conclusions It is feasible to use the V‐Net model to automatically segment cervical cancer CTV and OARs to achieve a more efficient radiotherapy workflow. In the delineation of most target areas and OARs, the performance of V‐net is better than U‐net. It also offers advantages with its feature of predicting the low‐dose area prospectively before radiation therapy (RT).

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“…Over the years, there have been attempts to develop automatic methods for small bowel segmentation, especially using deep learning. The small bowel was included in segmenting multiple organs-at-risk for radiotherapy treatment planning of affected tissues, such as pancreatic and cervical cancers, in CT scans [8,9,13]. Although the results obtained for the small bowel are reasonable, some of their data included only the part of the small bowel that is closest to the target area, which needed to be dose-evaluated [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…The small bowel was included in segmenting multiple organs-at-risk for radiotherapy treatment planning of affected tissues, such as pancreatic and cervical cancers, in CT scans [8,9,13]. Although the results obtained for the small bowel are reasonable, some of their data included only the part of the small bowel that is closest to the target area, which needed to be dose-evaluated [8,9]. In [13], the rough bowel location was detected instead of performing pixel-accurate small bowel segmentation.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation

Shin¹,

Lee²,

Summers³

2021

Preprint

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We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement. To make the domain adaptation more controllable, we disentangle intensity and non-intensity features within a unique two-stream auto-encoding architecture, and selectively adapt the non-intensity features that are believed to be more transferable across domains. The segmentation prediction is performed by aggregating the disentangled features. We evaluated our method using intravenous contrast-enhanced abdominal CT scans with and without oral contrast, which are used as source and target domains, respectively. The proposed method showed clear improvements in terms of three different metrics compared to other domain adaptation methods that are without the feature disentanglement. The method brings small bowel segmentation closer to clinical application.

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Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples

Cited by 15 publications

References 20 publications

Clinical validation of an automatic atlas‐based segmentation tool for male pelvis CT images

Clinical validation of an automatic atlas‐based segmentation tool for male pelvis CT images

Three‐dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images

Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation

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