These figures are the graphical results of my Master 2 internship on automatic segmentation using SAM2(Segment Anything Model 2) an artificial intelligence. The red line represents the best cell line from which anatomical measurements were made.

Seven Segment V2

## Overview

Seven Segment V2 is a dataset for object detection tasks - it contains Number annotations for 742 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Visual comparison of 100 human annotations (labels) compared with Segment Anything Model 2 (SAM2) segmentation.

Document Segmentation V2

## Overview

Document Segmentation V2 is a dataset for instance segmentation tasks - it contains Document K3N7 annotations for 1,763 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Few-Shot Segmentation (FSS) aims to learn class-agnostic segmentation on few classes to segment arbitrary classes, but at the risk of overfitting. To address this, some methods use the well-learned knowledge of foundation models (e.g., SAM) to simplify the learning process. Recently, SAM 2 has extended SAM by supporting video segmentation, whose class-agnostic matching ability is useful to FSS. A simple idea is to encode support foreground (FG) features as memory, with which query FG features are matched and fused. Unfortunately, the FG objects in different frames of SAM 2's video data are always the same identity, while those in FSS are different identities, i.e., the matching step is incompatible. Therefore, we design Pseudo Prompt Generator to encode pseudo query memory, matching with query features in a compatible way. However, the memories can never be as accurate as the real ones, i.e., they are likely to contain incomplete query FG, but some unexpected query background (BG) features, leading to wrong segmentation. Hence, we further design Iterative Memory Refinement to fuse more query FG features into the memory, and devise a Support-Calibrated Memory Attention to suppress the unexpected query BG features in memory. Extensive experiments have been conducted on PASCAL-5i and COCO-20i to validate the effectiveness of our design, e.g., the 1-shot mIoU can be 4.2% better than the best baseline.

Doodleverse/Segmentation Zoo Res-UNet models for Aerial/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images

These Residual-UNet models have been created using Segmentation Gym*

Image size used by model: 1024 x 768 x 3 pixels

classes:

water

other

File descriptions

For each model, there are 5 files with the same root name:

1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.

2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function train_model.py. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function seg_images_in_folder.py. Models may be ensembled.

3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the config file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model

4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function train_model.py

5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function train_model.py

Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU

References

*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

Doodleverse/Segmentation Gym Res-UNet models for 2-class (water, other) segmentation of CoastCam runup timestack imagery

This model release is part of the Doodleverse: https://github.com/Doodleverse

These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels.

Models have been created using Segmentation Gym* using an as-yet unpublished dataset of images and associated label images. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference

Classes: {0=other, 1=water}

File descriptions

There are two models; v7 has been trained from scratch, and v8 has been fine-tuned using hyperparameter adjustment. For each model, there are 5 files with the same root name:

1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.

2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function train_model.py. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function seg_images_in_folder.py. Models may be ensembled.

3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the config file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model

4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function train_model.py

5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function train_model.py

Additionally,

1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU
2. sample_images.zip contains a few example input files, for model testing

References

*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

stodoran/elwha-segmentation-v2 dataset hosted on Hugging Face and contributed by the HF Datasets community

Bags Segmentation V2

## Overview

Bags Segmentation V2 is a dataset for instance segmentation tasks - it contains Bag Vf25 annotations for 300 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.

These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels.

Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7384242

Classes: {0=other, 1=water}

File descriptions

For each model, there are 5 files with the same root name:

1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.

2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function train_model.py. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function seg_images_in_folder.py. Models may be ensembled.

3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the config file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model

4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function train_model.py

5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function train_model.py

Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU

References

*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

** Buscombe, D. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7384242

The TotalSegmentator-V2 dataset is a publicly available dataset for 3D medical image segmentation. It contains 1,228 CT scans with annotations for 117 major anatomical structures in WBCT images.

Reference field boundaries dataset generated in the paper "FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery".

A hand-annotated field boundary dataset (2022) covering 8 10x10 km areas across the world is made available. The study areas are located in Argentina, Australia, Brazil, China, South Africa, Spain, USA-California, and USA-Iowa.

This dataset contains two files:

• reference_field_boundaries.gpkg: hand-annotated dataset, with polygons defining the field boundaries.
• study_areas.gpkg: polygons defining the limits of the study areas and additional metadata about each area.

More information on how this dataset was prepared is available in the paper "FieldSeg: A scalable agricultural field extraction framework based on the Segment Anything Model and 10-m Sentinel-2 imagery".

Dataset

This dataset was created by Kumar Shubham

Released under CC0: Public Domain

Contents

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other)

Description

3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, near-infrared, and short-wave infrared bands only

These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.

Two data sources have been combined

Dataset 1

• 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571
• Labels have been reclassified from 4 classes to 2 classes.
• Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.
• These images and labels have been made using the Doodleverse software package, Doodler*.

Dataset 2

3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******

A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water

File descriptions

classes.txt, a file containing the class names

images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents

labels.zip, a zipped folder containing the 1-band label images

nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images

swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images

overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)

resized_images.zip, RGB images resized to 512x512x3 pixels

resized_labels.zip, label images resized to 512x512x1 pixels

resized_nir.zip, NIR images resized to 512x512x1 pixels

resized_swir.zip, SWIR images resized to 512x512x1 pixels

References

*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.

**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information

****Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571

*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/

******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.

Skittle Segmentation V2

## Overview

Skittle Segmentation V2 is a dataset for instance segmentation tasks - it contains Objects annotations for 815 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 RGB images of coasts.

Based on SWED*** data

https://openmldata.ukho.gov.uk/

These Residual-UNet model data are based on images of coasts and associated labels. Models have been fitted to the following types of data

1. RGB (3 band): red, green, blue

Classes are: {0: null, 1: water}.

These files are used in conjunction with Segmentation Zoo*

For each model, there are 3 files with the same root name:

1. '.json' config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.

2. '.h5' weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images

3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model

References

* https://github.com/Doodleverse/segmentation_zoo

** https://github.com/Doodleverse/segmentation_gym

*** https://www.sciencedirect.com/science/article/abs/pii/S0034425722001584

We release MarsData-V2

The stream geomorphic assessment (SGA) is a physical assessment competed by geomorphologists to determine the condition and sensitivity of a stream. The SGA Phase 2 Segment Breaks are points that indicate where the Phase 1 SGA reach was "segmented" into smaller Phase 2 segments. These segments are determined in the field and are based on changes in topography, slope and valley setting that were not found in phase 1, and on changes in condition found in the field. Where there found in the field a significant change in any of the above there is a segment break created.

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)

Description

4088 images and 4088 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue bands only

These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.

Two data sources have been combined

Dataset 1

• 1018 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7335647
• Labels have been reclassified from 4 classes to 2 classes.
• Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.
• These images and labels have been made using the Doodleverse software package, Doodler*.

Dataset 2

• 3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******
• A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water

File descriptions

• classes.txt, a file containing the class names
• images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents
• labels.zip, a zipped folder containing the 1-band label images
• overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, bllue=0=other)
• resized_images.zip, RGB images resized to 512x512x3 pixels
• resized_labels.zip, label images resized to 512x512x1 pixels

References

*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.

**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information

****Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647

*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/

******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.

The Imaging Data Commons (IDC)(https://imaging.datacommons.cancer.gov/) [1] connects researchers with publicly available cancer imaging data, often linked with other types of cancer data. Many of the collections have limited annotations due to the expense and effort required to create these manually. The increased capabilities of AI analysis of radiology images provide an opportunity to augment existing IDC collections with new annotation data. To further this goal, we trained several nnUNet [2] based models for a variety of radiology segmentation tasks from public datasets and used them to generate segmentations for IDC collections.

To validate the model's performance, roughly 10% of the AI predictions were assigned to a validation set. For this set, a board-certified radiologist graded the quality of AI predictions on a Likert scale. If they did not 'strongly agree' with the AI output, the reviewer corrected the segmentation.

This record provides the AI segmentations, Manually corrected segmentations, and Manual scores for the inspected IDC Collection images.

Only 10% of the AI-derived annotations provided in this dataset are verified by expert radiologists . More details, on model training and annotations are provided within the associated manuscript to ensure transparency and reproducibility.

This work was done in two stages. Versions 1.x of this record were from the first stage. Versions 2.x added additional records. In the Version 1.x collections, a medical student (non-expert) reviewed all the AI predictions and rated them on a 5-point Likert Scale, for any AI predictions in the validation set that they did not 'strongly agree' with, the non-expert provided corrected segmentations. This non-expert was not utilized for the Version 2.x additional records.

Likert Score Definition:

Guidelines for reviewers to grade the quality of AI segmentations.

5 Strongly Agree - Use-as-is (i.e., clinically acceptable, and could be used for treatment without change)

4 Agree - Minor edits that are not necessary. Stylistic differences, but not clinically important. The current segmentation is acceptable

3 Neither agree nor disagree - Minor edits that are necessary. Minor edits are those that the review judges can be made in less time than starting from scratch or are expected to have minimal effect on treatment outcome

2 Disagree - Major edits. This category indicates that the necessary edit is required to ensure correctness, and sufficiently significant that user would prefer to start from the scratch

1 Strongly disagree - Unusable. This category indicates that the quality of the automatic annotations is so bad that they are unusable.

Zip File Folder Structure

Each zip file in the collection correlates to a specific segmentation task. The common folder structure is

ai-segmentations-dcm This directory contains the AI model predictions in DICOM-SEG format for all analyzed IDC collection files

qa-segmentations-dcm This directory contains manual corrected segmentation files, based on the AI prediction, in DICOM-SEG format. Only a fraction, ~10%, of the AI predictions were corrected. Corrections were performed by radiologist (rad*) and non-experts (ne*)

qa-results.csv CSV file linking the study/series UIDs with the ai segmentation file, radiologist corrected segmentation file, radiologist ratings of AI performance.

qa-results.csv Columns

The qa-results.csv file contains metadata about the segmentations, their related IDC case image, as well as the Likert ratings and comments by the reviewers.

Column

Description

Collection

The name of the IDC collection for this case

PatientID

PatientID in DICOM metadata of scan. Also called Case ID in the IDC

StudyInstanceUID

StudyInstanceUID in the DICOM metadata of the scan

SeriesInstanceUID

SeriesInstanceUID in the DICOM metadata of the scan

Validation

true/false if this scan was manually reviewed

Reviewer

Coded ID of the reviewer. Radiologist IDs start with ‘rad’ non-expect IDs start with ‘ne’

AimiProjectYear

2023 or 2024, This work was split over two years. The main methodology difference between the two is that in 2023, a non-expert also reviewed the AI output, but a non-expert was not utilized in 2024.

AISegmentation

The filename of the AI prediction file in DICOM-seg format. This file is in the ai-segmentations-dcm folder.

CorrectedSegmentation

The filename of the reviewer-corrected prediction file in DICOM-seg format. This file is in the qa-segmentations-dcm folder. If the reviewer strongly agreed with the AI for all segments, they did not provide any correction file.

Was the AI predicted ROIs accurate?

This column appears one for each segment in the task for images from AimiProjectYear 2023. The reviewer rates segmentation quality on a Likert scale. In tasks that have multiple labels in the output, there is only one rating to cover them all.

Was the AI predicted {SEGMENT_NAME} label accurate?

This column appears one for each segment in the task for images from AimiProjectYear 2024. The reviewer rates each segment for its quality on a Likert scale.

Do you have any comments about the AI predicted ROIs?

Open ended question for the reviewer

Do you have any comments about the findings from the study scans?

Open ended question for the reviewer

File Overview

brain-mr.zip

Segment Description: brain tumor regions: necrosis, edema, enhancing

IDC Collection: UPENN-GBM

Links: model weights, github

breast-fdg-pet-ct.zip

Segment Description: FDG-avid lesions in breast from FDG PET/CT scans QIN-Breast

IDC Collection: QIN-Breast

Links: model weights, github

breast-mr.zip

Segment Description: Breast, Fibroglandular tissue, structural tumor

IDC Collection: duke-breast-cancer-mri

Links: model weights, github

kidney-ct.zip

Segment Description: Kidney, Tumor, and Cysts from contrast enhanced CT scans

IDS Collection: TCGA-KIRC, TCGA-KIRP, TCGA-KICH, CPTAC-CCRCC

Links: model weights, github

liver-ct.zip

Segment Description: Liver from CT scans

IDC Collection: TCGA-LIHC

Links: model weights, github

liver2-ct.zip

Segment Description: Liver and Lesions from CT scans

IDC Collection: HCC-TACE-SEG, COLORECTAL-LIVER-METASTASES

Links: model weights, github

liver-mr.zip

Segment Description: Liver from T1 MRI scans

IDC Collection: TCGA-LIHC

Links: model weights, github

lung-ct.zip

Segment Description: Lung and Nodules (3mm-30mm) from CT scans

IDC Collections:

Anti-PD-1-Lung

LUNG-PET-CT-Dx

NSCLC Radiogenomics

RIDER Lung PET-CT

TCGA-LUAD

TCGA-LUSC

Links: model weights 1, model weights 2, github

lung2-ct.zip

Improved model version

Segment Description: Lung and Nodules (3mm-30mm) from CT scans

IDC Collections:

QIN-LUNG-CT, SPIE-AAPM Lung CT Challenge

Links: model weights, github

lung-fdg-pet-ct.zip

Segment Description: Lungs and FDG-avid lesions in the lung from FDG PET/CT scans

IDC Collections:

ACRIN-NSCLC-FDG-PET

Anti-PD-1-Lung

LUNG-PET-CT-Dx

NSCLC Radiogenomics

RIDER Lung PET-CT

TCGA-LUAD

TCGA-LUSC

Links: model weights, github

prostate-mr.zip

Segment Description: Prostate from T2 MRI scans

IDC Collection: ProstateX, Prostate-MRI-US-Biopsy

Links: model weights, github

Changelog

2.0.2 - Fix the brain-mr segmentations to be transformed correctly

2.0.1 - added AIMI 2024 radiologist comments to qa-results.csv

2.0.0 - added AIMI 2024 segmentations

1.X - AIMI 2023 segmentations and reviewer scores