3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review

Kern, Daria; Mastmeyer, André

doi:10.36227/techrxiv.13360259.v1

Cited by 6 publications

(5 citation statements)

References 62 publications

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“…This step is achieved via the modulation map F m ∈ R w×h×d×n (NVB object attention feature map) derived from I Img . We call these rough estimates of the NVB locations as bounding boxes 24 or candidate volumes-of -interest (VOIs), which are denoted as {B i ∈ R 3 } i = 1,2 , where i = 1 represents the left NVB and i = 2 represents the right NVB. B i is represented as [x i c , y i c , z i c ], where x i c , y i c , and z i c denote the bounding box's center for NVB i.…”

Section: Overviewmentioning

confidence: 99%

Automatic segmentation of neurovascular bundle on mri using deep learning based topological modulated network

et al. 2023

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PurposeRadiation damage on neurovascular bundles (NVBs) may be the cause of sexual dysfunction after radiotherapy for prostate cancer. However, it is challenging to delineate NVBs as organ‐at‐risks from planning CTs during radiotherapy. Recently, the integration of MR into radiotherapy made NVBs contour delineating possible. In this study, we aim to develop an MRI‐based deep learning method for automatic NVB segmentation.MethodsThe proposed method, named topological modulated network, consists of three subnetworks, that is, a focal modulation, a hierarchical block and a topological fully convolutional network (FCN). The focal modulation is used to derive the location and bounds of left and right NVBs’, namely the candidate volume‐of‐interests (VOIs). The hierarchical block aims to highlight the NVB boundaries information on derived feature map. The topological FCN then segments the NVBs inside the VOIs by considering the topological consistency nature of the vascular delineating. Based on the location information of candidate VOIs, the segmentations of NVBs can then be brought back to the input MRI's coordinate system.ResultsA five‐fold cross‐validation study was performed on 60 patient cases to evaluate the performance of the proposed method. The segmented results were compared with manual contours. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) are (left NVB) 0.81 ± 0.10, 1.49 ± 0.88 mm, and (right NVB) 0.80 ± 0.15, 1.54 ± 1.22 mm, respectively.ConclusionWe proposed a novel deep learning‐based segmentation method for NVBs on pelvic MR images. The good segmentation agreement of our method with the manually drawn ground truth contours supports the feasibility of the proposed method, which can be potentially used to spare NVBs during proton and photon radiotherapy and thereby improve the quality of life for prostate cancer patients.

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Section: Overviewmentioning

confidence: 99%

Automatic segmentation of neurovascular bundle on mri using deep learning based topological modulated network

et al. 2023

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“…Object detection is an important link in medical image processing, usually using a square frame to mark and locate areas of interest such as lesions and organs, which is a preprocessing step for further segmentation or classification. Especially for small target lesions, locking the location of the lesions in advance and storing only the surrounding areas for semantic segmentation are conducive to reducing storage consumption and improving the accuracy of segmentation ( Kern and Mastmeyer, 2021 ). It can be divided into detection of 2D MRI slices and 3D MRI image sets.…”

Section: Ai Achievements In Medical Imaging and Organoidsmentioning

confidence: 99%

“…It can be divided into detection of 2D MRI slices and 3D MRI image sets. The object detection of 2D images is to feed each slice of the MRI into the training network separately, which can obtain more training data and correspondingly more training volume than 3D object detection ( Kern and Mastmeyer, 2021 ). But the disadvantage is that the context information will be lost.…”

Section: Ai Achievements In Medical Imaging and Organoidsmentioning

confidence: 99%

Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip

Gao

Wang²,

Li³

et al. 2022

Front. Bioeng. Biotechnol.

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Organ-on-a-chip (OOC) is a new type of biochip technology. Various types of OOC systems have been developed rapidly in the past decade and found important applications in drug screening and precision medicine. However, due to the complexity in the structure of both the chip-body itself and the engineered-tissue inside, the imaging and analysis of OOC have still been a big challenge for biomedical researchers. Considering that medical imaging is moving towards higher spatial and temporal resolution and has more applications in tissue engineering, this paper aims to review medical imaging methods, including CT, micro-CT, MRI, small animal MRI, and OCT, and introduces the application of 3D printing in tissue engineering and OOC in which medical imaging plays an important role. The achievements of medical imaging assisted tissue engineering are reviewed, and the potential applications of medical imaging in organoids and OOC are discussed. Moreover, artificial intelligence - especially deep learning - has demonstrated its excellence in the analysis of medical imaging; we will also present the application of artificial intelligence in the image analysis of 3D tissues, especially for organoids developed in novel OOC systems.

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“…E.g. here, the random regression forests (RRF) are used to detect organ bounding boxes in CT data, while other approached are ready for usage [Kern and Mastmeyer, 2022]. The U-Nets make use of the detected bounding and segment the contained organs.…”

Section: Training Of the Modelsmentioning

confidence: 99%

Concept for Automatic Multi-object Organ Detection and Segmentation in Abdominal CT Data

Mastmeyer¹

2022

CEM

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The ability to generate 3D patient models in a fast and reliable way, is of great importance, e.g. for the simulation of liver punctures in a virtual reality simulation. The aim is to automatically detect and segment abdominal structures in CT scans. In particular among the selected organ group, the pancreas poses a challenge. We use a combination of random regression forests and U-Nets to detect bounding boxes and generate segmentation masks for five abdominal organs (liver, kidneys, spleen, and pancreas). Proof of concept training and testing was carried out on 50 CT scans from various public sources. Preliminary results showed Dice coefficients of up to 0.71. The proposed method can theoretically be used for any anatomical structure, as long as sufficient training data is available.

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3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review

Cited by 6 publications

References 62 publications

Automatic segmentation of neurovascular bundle on mri using deep learning based topological modulated network

Automatic segmentation of neurovascular bundle on mri using deep learning based topological modulated network

Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip

Concept for Automatic Multi-object Organ Detection and Segmentation in Abdominal CT Data

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