Outlier guided optimization of abdominal segmentation

Xu, Yuchen; Tang, Olivia; Tang, Yucheng; Lee, Ho Hin; Chen, Yunqiang; Gao, David Yang; Han, Shizhong; Gao, Riqiang; Savona, Michael R.; Abramson, Richard G.; Huo, Yuankai; Landman, Bennett A.

doi:10.1117/12.2549365

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Yet, the majority of these studies didn’t investigate models’ performance on unseen external datasets. Xu et al (24) focused on the occurrence of outliers during image segmentation and how to solve this problem. Recent studies addressed the limitations and benefits of DL-based organ segmentation in real-life clinical scenarios (14, 25).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-assisted multiple organ segmentation from whole-body CT images

Salimi,

Shiri,

Mansouri

et al. 2023

Preprint

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Background: Automated organ segmentation from computed tomography (CT) images facilitates a number of clinical applications, including clinical diagnosis, monitoring of treatment response, quantification, radiation therapy treatment planning, and radiation dosimetry. Purpose: To develop a novel deep learning framework to generate multi-organ masks from CT images for 23 different body organs. Methods: A dataset consisting of 3106 CT images (649,398 axial 2D CT slices, 13,640 images/segment pairs) and ground-truth manual segmentation from various online available databases were collected. After cropping them to body contour, they were resized, normalized and used to train separate models for 23 organs. Data were split to train (80%) and test (20%) covering all the databases. A Res-UNET model was trained to generate segmentation masks from the input normalized CT images. The model output was converted back to the original dimensions and compared with ground-truth segmentation masks in terms of Dice and Jaccard coefficients. The information about organ positions was implemented during post-processing by providing six anchor organ segmentations as input. Our model was compared with the online available TotalSegmentator model through testing our model on their test datasets and their model on our test datasets. Results: The average Dice coefficient before and after post-processing was 84.28% and 83.26% respectively. The average Jaccard index was 76.17 and 70.60 before and after post-processing respectively. Dice coefficients over 90% were achieved for the liver, heart, bones, kidneys, spleen, femur heads, lungs, aorta, eyes, and brain segmentation masks. Post-processing improved the performance in only nine organs. Our model on the TotalSegmentator dataset was better than their models on our dataset in five organs out of 15 common organs and achieved almost similar performance for two organs. Conclusions: The availability of a fast and reliable multi-organ segmentation tool leverages implementation in clinical setting. In this study, we developed deep learning models to segment multiple body organs and compared the performance of our models with different algorithms. Our model was trained on images presenting with large variability emanating from different databases producing acceptable results even in cases with unusual anatomies and pathologies, such as splenomegaly. We recommend using these algorithms for organs providing good performance. One of the main merits of our proposed models is their lightweight nature with an average inference time of 1.67 seconds per case per organ for a total-body CT image, which facilitates their implementation on standard computers.

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Section: Introductionmentioning

confidence: 99%

Deep learning-assisted multiple organ segmentation from whole-body CT images

Salimi,

Shiri,

Mansouri

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Historically, these studies provided compelling investigations on specific kidney donors but have not yet looked at multiple renal substructures in healthy kidneys. In recent studies, several kidney segmentation methods have been proposed and have achieved promising performance boosted by deep neural networks [14][15][16][17]. 3D methods were also explored for medical image segmentations [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Renal cortex, medulla and pelvicaliceal system segmentation on arterial phase CT images with random patch-based networks

Tang

Gao

Lee

et al. 2021

Medical Imaging 2021: Image Processing

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Renal segmentation on contrast-enhanced computed tomography (CT) provides distinct spatial context and morphology. Current studies for renal segmentations are highly dependent on manual efforts, which are time-consuming and tedious. Hence, developing an automatic framework for the segmentation of renal cortex, medulla and pelvicalyceal system is an important quantitative assessment of renal morphometry. Recent innovations in deep methods have driven performance toward levels for which clinical translation is appealing. However, the segmentation of renal structures can be challenging due to the limited field-of-view (FOV) and variability among patients. In this paper, we propose a method to automatically label the renal cortex, the medulla and pelvicalyceal system. First, we retrieved 45 clinically-acquired deidentified arterial phase CT scans (45 patients, 90 kidneys) without diagnosis codes (ICD-9) involving kidney abnormalities. Second, an interpreter performed manual segmentation to pelvis, medulla and cortex slice-by-slice on all retrieved subjects under expert supervision. Finally, we proposed a patch-based deep neural networks to automatically segment renal structures. Compared to the automatic baseline algorithm (3D U-Net) and conventional hierarchical method (3D U-Net Hierarchy), our proposed method achieves improvement of 0.7968 to 0.6749 (3D U-Net), 0.7482 (3D U-Net Hierarchy) in terms of mean Dice scores across three classes (p-value < 0.001, paired t-tests between our method and 3D U-Net Hierarchy). In summary, the proposed algorithm provides a precise and efficient method for labeling renal structures.

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Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images

Selle,

Kircher,

Schwennen

et al. 2024

BMC Med Inform Decis Mak

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Background Unsupervised clustering and outlier detection are important in medical research to understand the distributional composition of a collective of patients. A number of clustering methods exist, also for high-dimensional data after dimension reduction. Clustering and outlier detection may, however, become less robust or contradictory if multiple high-dimensional data sets per patient exist. Such a scenario is given when the focus is on 3-D data of multiple organs per patient, and a high-dimensional feature matrix per organ is extracted. Methods We use principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and multiple co-inertia analysis (MCIA) combined with bagplots to study the distribution of multi-organ 3-D data taken by computed tomography scans. After point-set registration of multiple organs from two public data sets, multiple hundred shape features are extracted per organ. While PCA and t-SNE can only be applied to each organ individually, MCIA can project the data of all organs into the same low-dimensional space. Results MCIA is the only approach, here, with which data of all organs can be projected into the same low-dimensional space. We studied how frequently (i.e., by how many organs) a patient was classified to belong to the inner or outer 50% of the population, or as an outlier. Outliers could only be detected with MCIA and PCA. MCIA and t-SNE were more robust in judging the distributional location of a patient in contrast to PCA. Conclusions MCIA is more appropriate and robust in judging the distributional location of a patient in the case of multiple high-dimensional data sets per patient. It is still recommendable to apply PCA or t-SNE in parallel to MCIA to study the location of individual organs.

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Outlier guided optimization of abdominal segmentation

Cited by 3 publications

References 10 publications

Deep learning-assisted multiple organ segmentation from whole-body CT images

Deep learning-assisted multiple organ segmentation from whole-body CT images

Renal cortex, medulla and pelvicaliceal system segmentation on arterial phase CT images with random patch-based networks

Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images

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