CT-ORG, a new dataset for multiple organ segmentation in computed tomography

Rister, Blaine; Yi, Darvin; Shivakumar, Kaushik; Nobashi, Tomomi W.; Rubin, Daniel L.

doi:10.1038/s41597-020-00715-8

Cited by 78 publications

(65 citation statements)

References 18 publications

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“…However, it did not have pixel level annotations that outlined the liver. As a result, for the second part of this study where we train DL-based frameworks to segment the liver, we used the publicly available CT-ORG: CT volumes with multiple organ segmentation dataset [30,31] for which pixel level annotations were available.…”

Section: Methodsmentioning

confidence: 99%

“…We divided the publicly available CT-ORG: CT volumes with multiple organ segmentation dataset [30,31] into a training and generalization test set. CT-ORG comprises of 140 SECT scans with detailed pixel-level annotations of the liver, lungs, bones, kidneys, and bladder.…”

Section: Dataset Splits and Statistical Analysismentioning

confidence: 99%

“…Table 1 shows the scan parameters and patient characteristics that were made available with the dataset. Additional details about the CT-ORG dataset can be found in Rister et al's published report [30,45].…”

Section: Dataset Splits and Statistical Analysismentioning

confidence: 99%

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Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans

et al. 2022

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We map single energy CT (SECT) scans to synthetic dual-energy CT (synth-DECT) material density iodine (MDI) scans using deep learning (DL) and demonstrate their value for liver segmentation. A 2D pix2pix (P2P) network was trained on 100 abdominal DECT scans to infer synth-DECT MDI scans from SECT scans. The source and target domain were paired with DECT monochromatic 70 keV and MDI scans. The trained P2P algorithm then transformed 140 public SECT scans to synth-DECT scans. We split 131 scans into 60% train, 20% tune, and 20% held-out test to train four existing liver segmentation frameworks. The remaining nine low-dose SECT scans tested system generalization. Segmentation accuracy was measured with the dice coefficient (DSC). The DSC per slice was computed to identify sources of error. With synth-DECT (and SECT) scans, an average DSC score of 0.93±0.06 (0.89±0.01) and 0.89±0.01 (0.81±0.02) was achieved on the held-out and generalization test sets. Synth-DECT-trained systems required less data to perform as well as SECT-trained systems. Low DSC scores were primarily observed around the scan margin or due to non-liver tissue or distortions within ground-truth annotations. In general, training with synth-DECT scans resulted in improved segmentation performance with less data.

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Section: Methodsmentioning

confidence: 99%

Section: Dataset Splits and Statistical Analysismentioning

confidence: 99%

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Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans

et al. 2022

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“…on completely unseen datasets. Specifically, we use all cases available in CT-ORG [48] (https://wiki.cancerimagingarchive.net/display/ Public/CT-ORG%3A+CT+volumes+with+multiple+organ+segmentations, accessed on 28 May 2021) to evaluate the MO-Liverand MO-L.Kidneyon 139 and 137 test cases, respectively. We excluded cases that did not have liver annotations or surgically removed left kidneys.…”

Section: Datasetsmentioning

confidence: 99%

Going to Extremes: Weakly Supervised Medical Image Segmentation

Roth

Yang

et al. 2021

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Medical image annotation is a major hurdle for developing precise and robust machine-learning models. Annotation is expensive, time-consuming, and often requires expert knowledge, particularly in the medical field. Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation. An initial segmentation is generated based on the extreme points using the random walker algorithm. This initial segmentation is then used as a noisy supervision signal to train a fully convolutional network that can segment the organ of interest, based on the provided user clicks. Through experimentation on several medical imaging datasets, we show that the predictions of the network can be refined using several rounds of training with the prediction from the same weakly annotated data. Further improvements are shown using the clicked points within a custom-designed loss and attention mechanism. Our approach has the potential to speed up the process of generating new training datasets for the development of new machine-learning and deep-learning-based models for, but not exclusively, medical image analysis.

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“…There are 8 organs are annotated by a radiologist. The CT-ORG [30] is an open dataset which contains 140 CT images and 6 organs are annotated, most of these images come from a challenge training set [1]. The AbdomenCT-1K dataset [26] extended five open single-class organs annotation datasets to four classes (with 1062 volumes) and a small clinical dataset (with 50 volumes come from 20 patients).…”

Section: Related Work 21 Abdominal Organs Segmentation Datasetsmentioning

confidence: 99%