Efficient Web-Based Review for Automatic Segmentation of Volumetric DICOM Images

T, Stein; Metzger, Jasmin; Scherer, Jonas; Isensee, Fabian; Norajitra, Tobias; Kleesiek, Jens; Maier‐Hein, Klaus H.; Nolden, Marco

doi:10.1007/978-3-658-25326-4_33

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Therefore, the chief difference of this project from other similar works is that Nextmed has been developed with the idea of using these technologies in a hospital setting and on a daily basis. Whereas other studies [5][6][7][8][9][10][11][12] focused more exclusively on segmentation algorithms, this work includes these algorithms as part of a complete platform that addresses other issues. In relation with previous publications regarding the Nextmed project [32], main novelties are that the system has now been tested in greater detail by professionals from Salamanca Hospital, including radiologists, surgeons, and other specialists, as we wanted to obtain feedback from different points of view.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the automatic segmentation of anatomical structures is of considerable interest to medical practitioners, as it allows them to immediately start working, using 3D models that can facilitate clinical diagnoses. Although much research has been conducted on automatic segmentation [5][6][7][8][9][10][11][12], the use of a complete system capable of automatic segmentation on a large scale in hospitals, using traditional computers and unspecialized workstations and allowing augmented reality (AR) and virtual reality (VR) visualization, has not yet been achieved. Addressing this situation and providing an innovative visualization tool were the main objectives of this work and the result led to the Nextmed project [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality

Izard

Torres

Plaza

et al. 2020

Sensors

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The visualization of medical images with advanced techniques, such as augmented reality and virtual reality, represent a breakthrough for medical professionals. In contrast to more traditional visualization tools lacking 3D capabilities, these systems use the three available dimensions. To visualize medical images in 3D, the anatomical areas of interest must be segmented. Currently, manual segmentation, which is the most commonly used technique, and semi-automatic approaches can be time consuming because a doctor is required, making segmentation for each individual case unfeasible. Using new technologies, such as computer vision and artificial intelligence for segmentation algorithms and augmented and virtual reality for visualization techniques implementation, we designed a complete platform to solve this problem and allow medical professionals to work more frequently with anatomical 3D models obtained from medical imaging. As a result, the Nextmed project, due to the different implemented software applications, permits the importation of digital imaging and communication on medicine (dicom) images on a secure cloud platform and the automatic segmentation of certain anatomical structures with new algorithms that improve upon the current research results. A 3D mesh of the segmented structure is then automatically generated that can be printed in 3D or visualized using both augmented and virtual reality, with the designed software systems. The Nextmed project is unique, as it covers the whole process from uploading dicom images to automatic segmentation, 3D reconstruction, 3D visualization, and manipulation using augmented and virtual reality. There are many researches about application of augmented and virtual reality for medical image 3D visualization; however, they are not automated platforms. Although some other anatomical structures can be studied, we focused on one case: a lung study. Analyzing the application of the platform to more than 1000 dicom images and studying the results with medical specialists, we concluded that the installation of this system in hospitals would provide a considerable improvement as a tool for medical image visualization.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality

Izard

Torres

Plaza

et al. 2020

Sensors

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Efficient DICOM Image Tagging and Cohort Curation Within Kaapana

Kades¹,

Scherer²,

Scholtyssek³

et al. 2022

Informatik Aktuell

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Quantitative Imaging Informatics for Cancer Research

Fedorov

Beichel

Kalpathy–Cramer

et al. 2020

JCO Clinical Cancer Informatics

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PURPOSE We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program. METHODS QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach. RESULTS Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation. CONCLUSION Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.

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Efficient Web-Based Review for Automatic Segmentation of Volumetric DICOM Images

Cited by 3 publications

References 7 publications

Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality

Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality

Efficient DICOM Image Tagging and Cohort Curation Within Kaapana

Quantitative Imaging Informatics for Cancer Research

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