Deep learning based multi-organ segmentation and metastases segmentation in whole mouse body and the cryo-imaging cancer imaging and therapy analysis platform (CITAP)

Liu, Yiqiao; Gargesha, Madhu; Qutaish, Mohammed Q.; Zhou, Zhuxian; Scott, Bryan; Yousefi, Hamed; Zhang, Lu; Wilson, David L.

doi:10.1117/12.2549801

Cited by 7 publications

(3 citation statements)

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“…AIMOS as an enabling resource will be helpful in many areas of biomedical research, including tumor research [15][16][17]54 , organ lesion studies 18 , drug delivery 20 , and nanoparticle uptake [22][23][24][25][26] . It provides high-quality organ segmentations within a second.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-enabled multi-organ segmentation in whole-body mouse scans

et al. 2020

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Whole-body imaging of mice is a key source of information for research. Organ segmentation is a prerequisite for quantitative analysis but is a tedious and error-prone task if done manually. Here, we present a deep learning solution called AIMOS that automatically segments major organs (brain, lungs, heart, liver, kidneys, spleen, bladder, stomach, intestine) and the skeleton in less than a second, orders of magnitude faster than prior algorithms. AIMOS matches or exceeds the segmentation quality of state-of-the-art approaches and of human experts. We exemplify direct applicability for biomedical research for localizing cancer metastases. Furthermore, we show that expert annotations are subject to human error and bias. As a consequence, we show that at least two independently created annotations are needed to assess model performance. Importantly, AIMOS addresses the issue of human bias by identifying the regions where humans are most likely to disagree, and thereby localizes and quantifies this uncertainty for improved downstream analysis. In summary, AIMOS is a powerful open-source tool to increase scalability, reduce bias, and foster reproducibility in many areas of biomedical research.

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Section: Discussionmentioning

confidence: 99%

Deep learning-enabled multi-organ segmentation in whole-body mouse scans

et al. 2020

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“…We investigate the applicability of deep learning for segmenting mouse organs in block-face cryo-image volumes. There are no reports for such organ segmentation of block-face images, except for our very preliminary report 26 . AIMOS 25 proved that 2D U-Net is good for organ segmentation in mouse CT images.…”

Section: Introductionmentioning

confidence: 97%

Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks

Liu

Gargesha

Scott

et al. 2022

Sci Rep

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Cryo-imaging provided 3D whole-mouse microscopic color anatomy and fluorescence images that enables biotechnology applications (e.g., stem cells and metastatic cancer). In this report, we compared three methods of organ segmentation: 2D U-Net with 2D-slices and 3D U-Net with either 3D-whole-mouse or 3D-patches. We evaluated the brain, thymus, lung, heart, liver, stomach, spleen, left and right kidney, and bladder. Training with 63 mice, 2D-slices had the best performance, with median Dice scores of > 0.9 and median Hausdorff distances of < 1.2 mm in eightfold cross validation for all organs, except bladder, which is a problem organ due to variable filling and poor contrast. Results were comparable to those for a second analyst on the same data. Regression analyses were performed to fit learning curves, which showed that 2D-slices can succeed with fewer samples. Review and editing of 2D-slices segmentation results reduced human operator time from ~ 2-h to ~ 25-min, with reduced inter-observer variability. As demonstrations, we used organ segmentation to evaluate size changes in liver disease and to quantify the distribution of therapeutic mesenchymal stem cells in organs. With a 48-GB GPU, we determined that extra GPU RAM improved the performance of 3D deep learning because we could train at a higher resolution.

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“…Especially with the rise of deep learning, there have been a lot of techniques proposed over the past couple of years which are able to segment different regions of interest (ROIs) in a patient's body with high precision (e.g. [3,4,5,6,7]).…”

Section: Introductionmentioning

confidence: 99%

Automated Multi-Organ Segmentation in Pet Images Using Cascaded Training of a 3d U-Net and Convolutional Autoencoder

Liebgott

Lorenz

Gatidis

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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PET imaging is an important tool in clinical diagnostics, especially in oncology as it is able to visualize ongoing metabolic processes, e.g. caused by a tumor. Due to the low spatial resolution, a corresponding CT or MRI scan is normally necessary to gain knowledge about the physiological structures of a patient and especially to perform some computer-aided diagnostics methods, e.g. segmentation of structures of interest. As this transfer of information from CT/MRI to the PET domain is not always feasible, e.g. when the corresponding CT or MRI images are unavailable or corrupted by artifacts, we propose a novel approach to perform organ segmentation on the PET images directly. We utilize a CNN architecture based on a 3D U-Net combined with a convolutional autoencoder and train our model purely on PET images and corresponding ground truth masks. Our resulting Dice scores of 0.88, 0.82 an 0.59 for liver, spleen and spine, respectively, show that standalone PET organ segmentation is generally feasible.

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Deep learning based multi-organ segmentation and metastases segmentation in whole mouse body and the cryo-imaging cancer imaging and therapy analysis platform (CITAP)

Cited by 7 publications

References 0 publications

Deep learning-enabled multi-organ segmentation in whole-body mouse scans

Deep learning-enabled multi-organ segmentation in whole-body mouse scans

Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks

Automated Multi-Organ Segmentation in Pet Images Using Cascaded Training of a 3d U-Net and Convolutional Autoencoder

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