Remote sensing of environment research has explored the benefits of using synthetic aperture radar imagery systems for a wide range of land and marine applications since these systems are not affected by weather conditions and therefore are operable both daytime and nighttime. The design of image processing techniques for  synthetic aperture radar applications requires tests and validation on real and synthetic images. The GRSS benchmark database supports the desing and analysis of algorithms to deal with SAR and PolSAR data.

Last Updated On: 
Tue, 11/12/2019 - 10:38
Citation Author(s): 
Nobre, R. H.; Rodrigues, F. A. A.; Rosa, R.; Medeiros, F.N.; Feitosa, R., Estevão, A.A., Barros, A.S.

The dataset used in the paper "A Deep Learning Approach for Segmentation, Classification and Visualization of 3D High Frequency Ultrasound Images of Mouse Embryos" is provided here. It contains both the segmentation and classification images with manual labels. 

Instructions: 

The dataset contains the ultrasound mouse embyro images with manual labels. For more detail, please look into each subfolder and the paper "A Deep Learning Approach for Segmentation, Classification and Visualization of 3D High Frequency Ultrasound Images of Mouse Embryos". Or you can contact the author by zq415@nyu.edu if you have any question about the dataset and the paper. Thanks!

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Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures.

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This dataset is used to develop an algorithm for automatic segmenting the collected signals. When machining a workpiece in a milling process, vibration signals can be recorded by a 3-axis accelerometer, which is attached on the spindle of a CNC milling machine. To segment the recorded signals, a moving window (0.5 sec) is applied to sample the vibration signals and manually labeled the corresponding modes, i.e. dry run or milling, of each window. To verify the algorithm, 3 types of operations are provided and recorded in csv format. 

Instructions: 

Domain: milling process

Signal source: accelerometer 

Sampling rate: 2048 Hz 

Target: signal segmentation

Operations: 

  1. EXP_Drill: 
  • Spindle Speed (rpm): 2300
  • Feed Rate (mm/min): 190
  • Workpiece Material: SUS316
  • Ap (mm): 10
  • Ae (mm): 8.6
  • Tool Type: Drill
  • Tool Diameter (mm): 8.6
  • No. of Flutes: 2
  • No. of Files: 15

2.EXP_ISO 

  • Spindle Speed (rpm): 6000
  • Feed Rate (mm/min): 1500
  • Ap (mm): 5
  • Ae (mm): 5
  • Workpiece Material: AL6061
  • Tool Type: Milling
  • Tool Diameter (mm): 10
  • No. of Flutes: 3
  • No. of Files: 2

3.EXP_Square 

  • Spindle Speed (rpm): 1600
  • Feed Rate (mm/min): 320
  • Workpiece Material: FDAC
  • Ap (mm): 1
  • Ae (mm): 5
  • Tool Type: Milling
  • Tool Diameter (mm): 10
  • No. of Flutes: 4
  • No. of Files: 8

Total files: 25

File format: csv

Description of fields in each file

  • Timetag: tagging sequence per 0.5 sec
  • Segmentation: 0: dry run, 1: milling
  • Rawdata_X_1 ~ Rawdata_X_1024: vibration signals of axis X recorded at sampling rate 2048 Hz. It means the data collected in 0.5 sec.  
  • Rawdata_Y_1 ~ Rawdata_Y_1024: vibration signals of axis Y.
  • Rawdata_Z_1 ~ Rawdata_Z_1024: vibration signals of axis Z.  

 

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Pathologic Myopia Challenge (PALM), as a part of the serial challenge iChallenge, is organized as a half day Challenge, a Satellite Event of the ISBI 2019 conference in Venice, Italy. The PALM challenge focuses on the investigation and development of algorithms associated with the diagnosis of Pathological Myopia (PM) and segmentation of lesions in fundus photos from PM patients. The goal of the challenge is to evaluate and compare automated algorithms for the detection of pathological myopia on a common dataset of retinal fundus images.

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This dataset is a companion to a paper, "Segmentation Convolutional Neural Networks for Automatic Crater Detection on Mars" by DeLatte et al. 2019. DOI link: http://dx.doi.org/10.1109/JSTARS.2019.2918302

 

These are the segmentation target files for the three targets described in the paper: solid filled, thicker edge, and thinner edge. 

Instructions: 

These files match with the tiles that can be downloaded from the THEMIS Daytime IR Global Mosaic: http://www.mars.asu.edu/data/thm_dir/

Alternatively, this directory can be used for the download: http://www.mars.asu.edu/data/thm_dir/large/

Use this file pattern to grab the tiles:

  • 0 to +30N: thm_dir_N00_*.png
  • -30N to 0: thm_dir_N-30_*.png 

 

Included here are three targets for the 24 tiles ±30º latitude, 0-360º longitude. (Each tile is 30º by 30º, 7680 x 7680 pixels, and has a resolution of 256 pixels per degree). Craters with 2-32km radius are included, as identified by the Robbins & Hynek global Mars dataset (http://craters.sjrdesign.net/). The original data file for the crater locations and parameters can be found here: http://craters.sjrdesign.net/RobbinsCraterDatabase_20121016.tsv.zip 

Any arbitrary range of segmentation crater targets can be created using the file and python OpenCV.

 

To use for segmentation, download the corresponding THEMIS Daytime IR Global Mosaic tiles and this dataset can be used as the target images for segmentation. The filenames of the target files will match the filenames in the THEMIS Daytime IR Global Mosaic.

 

The file names for each type match the following patterns:

  • solid filled: thm_dir_N*_2_32_km_segrng.png
  • thicker edge (8): thm_dir_N*_2_32_km_segrng_8_edge.png
  • thinner edge (4): thm_dir_N*_2_32_km_segrng_4_edge.png

(segrng = segmentation range, referring to the 2-32 km radius range of craters in this dataset)

The numbers 4 and 8 above refer to the thickness parameter in python OpenCV. The circle drawing function is described here: https://docs.opencv.org/3.0-alpha/modules/imgproc/doc/drawing_functions....

 

 

 

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