Development of a volumetric pancreas segmentation CT dataset for AI applications through trained technologists: a study during the COVID 19 containment phase

Suman, Garima; Korfiatis, Panagiotis; Edwards, Marie E.; Garg, Sushil Kumar; Blezek, Daniel J.; Chari, Suresh T.; Goenka, Ajit H.

doi:10.1007/s00261-020-02741-x

Cited by 19 publications

(13 citation statements)

References 24 publications

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“…These regions of pancreas are difficult to delineate even for expert radiologists and often contribute to intra-reader and inter-reader variability. 44,45 The model-predicted mean (SD) pancreatic volumes were not different from the volumes derived from GT segmentations either in the internal test set (P = 0.08) or in the TCIA dataset (P = 0.3). Only one prior study reports results from an AI model-predicted pancreatic volumes.…”

Section: Discussionmentioning

confidence: 90%

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Two‐stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra‐reader and inter‐reader reliability at full and reduced radiation dose on an external dataset

et al. 2021

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To develop a two-stage three-dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on computed tomography (CT) and to further evaluate its performance in the context of intra-reader and inter-reader reliability at full dose and reduced radiation dose CTs on a public dataset. Methods: A dataset of 1994 abdomen CT scans (portal venous phase, slice thickness ≤ 3.75-mm, multiple CT vendors) was curated by two radiologists (R1 and R2) to exclude cases with pancreatic pathology, suboptimal image quality, and image artifacts (n = 77). Remaining 1917 CTs were equally allocated between R1 and R2 for volumetric pancreas segmentation [ground truth (GT)]. This internal dataset was randomly divided into training (n = 1380), validation (n = 248), and test (n = 289) sets for the development of a two-stage 3D CNN model based on a modified U-net architecture for automated volumetric pancreas segmentation. Model's performance for pancreas segmentation and the differences in model-predicted pancreatic volumes vs GT volumes were compared on the test set. Subsequently, an external dataset from The Cancer Imaging Archive (TCIA) that had CT scans acquired at standard radiation dose and same scans reconstructed at a simulated 25% radiation dose was curated (n = 41). Volumetric pancreas segmentation was done on this TCIA dataset by R1 and R2 independently on the full dose and then at the reduced radiation dose CT images. Intra-reader and inter-reader reliability, model's segmentation performance, and reliability between model-predicted pancreatic volumes at full vs reduced dose were measured. Finally, model's performance was tested on the benchmarking National Institute of Health (NIH)-Pancreas CT (PCT) dataset. Results: Three-dimensional CNN had mean (SD) Dice similarity coefficient (DSC): 0.91 (0.03) and average Hausdorff distance of 0.15 (0.09) mm on the test set. Model's performance was equivalent between males and females (P = 0.08) and across different CT slice thicknesses (P > 0.05) based on noninferiority statistical testing. There was no difference in model-predicted and GT pancreatic volumes [mean predicted volume 99 cc (31cc); GT volume 101 cc (33 cc), P = 0.33]. Mean pancreatic volume difference was À2.7 cc (percent difference: À2.4% of GT volume) with excellent correlation between model-predicted and GT volumes [concordance correlation coefficient (CCC)=0.97]. In the external TCIA dataset, the model had higher reliability than R1 and R2 on full vs reduced dose CT scans [model mean (SD) DSC: 0.96 (0.02), CCC = 0.995 vs R1 DSC: 0.83 (0.07), CCC = 0.89, and R2 DSC:0.87 (0.04), CCC = 0.97]. The DSC and volume concordance correlations for R1 vs R2 (inter-reader reliability) were 0.85 (0.07), CCC = 0.90 at full dose and 0.83 (0.07), CCC = 0.96 at reduced dose datasets. There was good reliability between model and R1 at both full and reduced dose CT [full dose: DSC: 0.81 (0.07), CCC = 0.83 and reduced dose DSC:0.81 (0.08), CCC = 0.87]. Likewise, there was good reliability betw...

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Section: Discussionmentioning

confidence: 90%

“…Segmentation errors were commonly seen at the interface of pancreatic head and duodenum and pancreatic tail and jejunum or splenic vessels. These regions of pancreas are difficult to delineate even for expert radiologists and often contribute to intra‐reader and inter‐reader variability 44,45 …”

Section: Discussionmentioning

confidence: 99%

Two‐stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra‐reader and inter‐reader reliability at full and reduced radiation dose on an external dataset

et al. 2021

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“…Hence, the preparation of CT scans is time-consuming. Future research needs to address the potential benefit of artificial intelligence for this part [24][25][26][27]. Once the G-code from CT data is deduced, the replica can easily be re-printed in a larger quantity, thereby opening the possibility for extended teaching and training purposes [28,29].…”

Section: Discussionmentioning

confidence: 99%

3D-Printed Replica and Porcine Explants for Pre-Clinical Optimization of Endoscopic Tumor Treatment by Magnetic Targeting

Roeth

Garretson

Beltz

et al. 2021

Cancers

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Background: Animal models have limitations in cancer research, especially regarding anatomy-specific questions. An example is the exact endoscopic placement of magnetic field traps for the targeting of therapeutic nanoparticles. Three-dimensional-printed human replicas may be used to overcome these pitfalls. Methods: We developed a transparent method to fabricate a patient-specific replica, allowing for a broad scope of application. As an example, we then additively manufactured the relevant organs of a patient with locally advanced pancreatic ductal adenocarcinoma. We performed experimental design investigations for a magnetic field trap and explored the best fixation methods on an explanted porcine stomach wall. Results: We describe in detail the eight-step development of a 3D replica from CT data. To guide further users in their decisions, a morphologic box was created. Endoscopies were performed on the replica and the resulting magnetic field was investigated. The best fixation method to hold the magnetic field traps stably in place was the fixation of loops at the stomach wall with endoscopic single-use clips. Conclusions: Using only open access software, the developed method may be used for a variety of cancer-related research questions. A detailed description of the workflow allows one to produce a 3D replica for research or training purposes at low costs.

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“… 25 [ 146 ] X-ray Data set based on previous literature 26 [ 59 ] X-ray JSRT/SCR dataset NLM( MC) Cohen dataset CoronaHack 27 [ 94 ] X-ray X-ray images from GitHub 28 [ 103 ] X-ray Shenzhen set NIH (National Institutes of Health, US) Clinical Center Hospital dataset COVID-Chest Xray-Dataset 29 [ 74 ] CT images Data set based on previous literature 30 [ 88 ] X-ray, CT images Hitherto dataset 31 [ 99 ] X-ray Github Kaggle 32 [ 134 ] US Nine COVID-19 patients 33 [ 102 ] CT images COVID-19 patient dataset. CT datasets of patients with different medical histories other than COVID-19 34 [ 148 ] CT images Radiologists’ review [ 128 ]. 35 [ 131 ] X-ray COVID-CT-Dataset [ 155 ] <...>…”

Section: Datasets and Imaging Modalities In The Surveyed Studiesmentioning

confidence: 99%

Medical image processing and COVID-19: A literature review and bibliometric analysis

Abumalloh¹,

Nilashi²,

Ismail³

et al. 2022

Journal of Infection and Public Health

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Development of a volumetric pancreas segmentation CT dataset for AI applications through trained technologists: a study during the COVID 19 containment phase

Cited by 19 publications

References 24 publications

Two‐stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra‐reader and inter‐reader reliability at full and reduced radiation dose on an external dataset

Two‐stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra‐reader and inter‐reader reliability at full and reduced radiation dose on an external dataset

3D-Printed Replica and Porcine Explants for Pre-Clinical Optimization of Endoscopic Tumor Treatment by Magnetic Targeting

Medical image processing and COVID-19: A literature review and bibliometric analysis

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