3D hemisphere-based convolutional neural network for whole-brain MRI segmentation

Yee, Evangeline; Ma, Da; Popuri, Karteek; Chen, Shuo; Lee, Hyun–Woo; Chow, Vincent; Ma, Cydney; Wang, Lei; Beg, Mirza Faisal

doi:10.1016/j.compmedimag.2021.102000

Cited by 13 publications

(2 citation statements)

References 45 publications

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“…The creation of 3D deep learning models necessitates numerous training parameters, leading to considerable computational overhead and potential overfitting hazards. [ 55 , 56 ]. Due to hardware limitations, 3D DL model neural network depth is typically shallower than that of 2D DL models.…”

Section: Discussionmentioning

confidence: 99%

Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis

Liu,

Sun,

Zhao

et al. 2023

BioMed Eng OnLine

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Purpose The contouring of organs at risk (OARs) in head and neck cancer radiation treatment planning is a crucial, yet repetitive and time-consuming process. Recent studies have applied deep learning (DL) algorithms to automatically contour head and neck OARs. This study aims to conduct a systematic review and meta-analysis to summarize and analyze the performance of DL algorithms in contouring head and neck OARs. The objective is to assess the advantages and limitations of DL algorithms in contour planning of head and neck OARs. Methods This study conducted a literature search of Pubmed, Embase and Cochrane Library databases, to include studies related to DL contouring head and neck OARs, and the dice similarity coefficient (DSC) of four categories of OARs from the results of each study are selected as effect sizes for meta-analysis. Furthermore, this study conducted a subgroup analysis of OARs characterized by image modality and image type. Results 149 articles were retrieved, and 22 studies were included in the meta-analysis after excluding duplicate literature, primary screening, and re-screening. The combined effect sizes of DSC for brainstem, spinal cord, mandible, left eye, right eye, left optic nerve, right optic nerve, optic chiasm, left parotid, right parotid, left submandibular, and right submandibular are 0.87, 0.83, 0.92, 0.90, 0.90, 0.71, 0.74, 0.62, 0.85, 0.85, 0.82, and 0.82, respectively. For subgroup analysis, the combined effect sizes for segmentation of the brainstem, mandible, left optic nerve, and left parotid gland using CT and MRI images are 0.86/0.92, 0.92/0.90, 0.71/0.73, and 0.84/0.87, respectively. Pooled effect sizes using 2D and 3D images of the brainstem, mandible, left optic nerve, and left parotid gland for contouring are 0.88/0.87, 0.92/0.92, 0.75/0.71 and 0.87/0.85. Conclusions The use of automated contouring technology based on DL algorithms is an essential tool for contouring head and neck OARs, achieving high accuracy, reducing the workload of clinical radiation oncologists, and providing individualized, standardized, and refined treatment plans for implementing "precision radiotherapy". Improving DL performance requires the construction of high-quality data sets and enhancing algorithm optimization and innovation.

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Section: Discussionmentioning

confidence: 99%

Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis

Liu,

Sun,

Zhao

et al. 2023

BioMed Eng OnLine

View full text Add to dashboard Cite

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“…Going further, 3D convolutional neural network (CNN)-based prognosis models have shown good performances in outcome prediction, actually of lung cancer and gliomas [66][67][68][69][70][71].…”

Section: Introductionmentioning

confidence: 99%

Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis

Pacella,

Brunese,

D’Imperio

et al. 2023

JCM

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Background: Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. CT-based imaging has a central role in detecting, staging, and managing PDAC. As several authors have proposed radiomics for risk stratification in patients undergoing surgery for PADC, in this narrative review, we have explored the actual fields of interest of radiomics tools in PDAC built on pre-surgical imaging and clinical variables, to obtain more objective and reliable predictors. Methods: The PubMed database was searched for papers published in the English language no earlier than January 2018. Results: We found 301 studies, and 11 satisfied our research criteria. Of those included, four were on resectability status prediction, three on preoperative pancreatic fistula (POPF) prediction, and four on survival prediction. Most of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to get predictive information in pre-surgical evaluation. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

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Multi-atlas subcortical segmentation: an orchestration of 3D fully convolutional network and generalized mixture function

Zhou

2023

Machine Vision and Applications

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3D hemisphere-based convolutional neural network for whole-brain MRI segmentation

Cited by 13 publications

References 45 publications

Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis

Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis

Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis

Multi-atlas subcortical segmentation: an orchestration of 3D fully convolutional network and generalized mixture function

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