Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs

Tang, Fan; Liang, Shujun; Zhong, Tao; Huang, Xia; Deng, Xiaogang; Zhang, Yu; Zhou, Linghong

doi:10.1007/s00330-019-06441-z

Cited by 25 publications

(18 citation statements)

References 27 publications

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“…Various publications were found with regard to the automated segmentation of gliomas in the post-operative setting [81][82][83][84]. Quantitative metrics are believed to be needed for therapy guidance, risk stratification, and outcome prognostication in the post-operative setting.…”

Section: Discussionmentioning

confidence: 99%

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

et al. 2021

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Objectives Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to provide an overview and meta-analysis of different MLA methods. Methods A systematic literature review and meta-analysis was performed on the eligible studies describing the segmentation of gliomas. Meta-analysis of the performance was conducted on the reported dice similarity coefficient (DSC) score of both the aggregated results as two subgroups (i.e., high-grade and low-grade gliomas). This study was registered in PROSPERO prior to initiation (CRD42020191033). Results After the literature search (n = 734), 42 studies were included in the systematic literature review. Ten studies were eligible for inclusion in the meta-analysis. Overall, the MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86). In addition, a DSC score of 0.83 (95% CI: 0.80–0.87) and 0.82 (95% CI: 0.78–0.87) was observed for the automated glioma segmentation of the high-grade and low-grade gliomas, respectively. However, heterogeneity was considerably high between included studies, and publication bias was observed. Conclusion MLAs facilitating automated segmentation of gliomas show good accuracy, which is promising for future implementation in neuroradiology. However, before actual implementation, a few hurdles are yet to be overcome. It is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set. Key Points • MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86), indicating a good performance. • MLA performance was comparable when comparing the segmentation results of the high-grade gliomas and the low-grade gliomas. • For future studies using MLAs, it is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.

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Section: Discussionmentioning

confidence: 99%

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

et al. 2021

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“…For example, Xue et al reported the performance outcomes of 3D convolution network in the detection of brain metastasis [8]; Tang et al suggested that automatic segmentation using DL could delineate the volume of the brain glioma for radiotherapy [9]. Thus, AI and DL can replace much of what doctors and health care providers used to do.…”

Section: Discussionmentioning

confidence: 99%

Medical Student Education Through Flipped Learning and Virtual Rotations in Radiation Oncology During the COVID-19 Pandemic: A Cross Sectional Research

Kim

Yoon

et al. 2021

Preprint

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BackgroundThe COVID-19 pandemic has stripped many medical students worldwide of their right to quality education. In response, we developed hybrid courses involving aspects of both online and in-person teaching for radiation oncology medical student clerkship.MethodsWe entitled students to customize their own rotation schedule using Google Forms and developed a flipped learning online class, which consisted of at least one video clip on basic knowledge of radiation oncology per day (yonsei-radonc.com). Students were instructed to watch online videos before the next day’s discussion session. Required components of the medical education program (e.g., target drawing, site visits to treatment facilities) were also prepared and conducted in accordance with the appropriate level of social distancing measures. Finally, we conducted questionnaire surveys after the completion of the week-long course and clerkship.ResultsFrom March to June 2020, 110 fourth-year medical students undertook a clinical module in our one-week radiation oncology program course. Each day, students completed the flipped learning prior to meeting with the educator and then participated in the online discussion session and conference. All activities were well performed as scheduled. Students’ motivation was high, as was their overall satisfaction with the course. The students were satisfied with the online contents, flipped learning strategy, and instructors.ConclusionsWe successfully integrated open and virtual educational platforms to improve access to and satisfaction with student clerkship. In the future “new normal,” minimized face-to-face learning interactions, such as flipped learning, should be actively utilized for medical and other students’ education.

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“…Studies have found that the rate of visits for colonic and rectal polyps in China has been low, which leaves hidden dangers for the health of the digestive system. Polyps can grow throughout the digestive tract, including the colon and rectum [15,16]. ere are few small intestinal polyps, and they are called colon polyps until their pathological properties are determined [17].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Automatic Detection of Rectal Polyps Using Abdominal CT Images Guided by Cold Snare Polypectomy

Lin

Chen

et al. 2021

Scientific Programming

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The study drew attention to the therapeutic effects of cold snare polypectomy guided by a deep convolutional neural network- (CNN-) based abdominal CT and hot snare polypectomy (HSP) on colonic and rectal polyps. Specifically, 90 patients were enrolled into a blank group, a control (Ctrl) group, and an experimental group. The blank group accepted HSP, the Ctrl accepted cold snare polypectomy, and the experimental group accepted cold snare polypectomy guided by deep CNN-based CT images. It was found that the experimental group had the lowest false-positive rate (9.2%) in polyp detection in contrast with the Ctrl (21.4%) and the blank group (52.3%) P < 0.05 . The complete resection rate of large polyps in the experimental group was the highest P < 0.05 , and its operation time (2.91 ± 0.75 min) was obviously shorter versus the blank group (6.18 ± 1.19 min) P < 0.05 . In conclusion, the cold snare polypectomy under the guidance of deep CNN-based CT has a relatively high complete resection rate and detection accuracy of polyps with a low complication rate, which can be adopted clinically.

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Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs

Cited by 25 publications

References 27 publications

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

Medical Student Education Through Flipped Learning and Virtual Rotations in Radiation Oncology During the COVID-19 Pandemic: A Cross Sectional Research

Deep Learning-Based Automatic Detection of Rectal Polyps Using Abdominal CT Images Guided by Cold Snare Polypectomy

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