top of page

Serial section Transmission Electron Microscopy image (ssTEM) Dataset

Updated: Jun 14, 2023

The dataset contains 30 ssTEM (serial section Transmission Electron Microscopy) images taken from the Drosophila larva ventral nerve cord (VNC). The images represent a set of consecutive slices within one 3D volume. Corresponding segmentation ground truths are also provided in this dataset.



Download data from

It wasn't possible to download from the EM segmentation challenge dataset

or from

Dataset details

Training and test data are comprised of three 512x512x30 TIF volumes (test-volume.tif, train-volume.tif and train-labels.tif). Files test-volume.tif and train-volume.tif contain grayscale 2D slices to be segmented. Additionally, training masks are provided in train-labels.tif as a 512x512x30 TIF volume.

The term "fold" could be used to refer to a specific partition or grouping of the training data. Each fold would contain a subset of the training data.

Additional details:

train-volume.tif (7.5 MB) |Original training image, 8-bit grayscale, 512x512x30 pixels|

train-labels.tif (7.5 MB)| Training image labels (0 - membranes, 255 - non-membranes), 8-bit grayscale, 512x512x30 pixels

test-volume.tif (7.5 MB) |Test image, 8-bit grayscale, 512x512x30 pixels The training and test datasets are two stacks of 30 sections from a serial section Transmission Electron Microscopy (ssTEM) data set of the Drosophila first instar larva ventral nerve cord (VNC). The microcube measures 2 x 2 x 1.5 microns approx., with a resolution of 4x4x50 nm/pixel.

Preprocessing the data

The UNet script operates on data from the ISBI Challenge, the dataset originally employed in the UNet paper.

Preprocessing the data with

python --data_dir /data

the sample output looks as follows


DLL 2023-06-12 06:03:29.213088 - (700, 1000) train_ce_loss : 0.284275084733963 train_dice_loss : 0.1452435553073883 train_total_loss : 0.4295186400413513

DLL 2023-06-12 06:21:17.149753 - (800, 1000) train_ce_loss : 0.20779138803482056 train_dice_loss : 0.11122724413871765 train_total_loss : 0.3190186321735382

DLL 2023-06-12 06:39:02.915866 - (900, 1000) train_ce_loss : 0.19838161766529083 train_dice_loss : 0.0974794328212738 train_total_loss : 0.29586106538772583

DLL 2023-06-12 06:57:04.944396 - (1000, 1000) train_ce_loss : 0.19091825187206268 train_dice_loss : 0.09579619765281677 train_total_loss : 0.28671443462371826

No fold specified for evaluation. Please use --fold [int] to select a fold.


[1].For the error

"File "/workspace/unet/model/", line 51, in TFTRTModel


TypeError: function() got an unexpected keyword argument 'jit_compile' "

You can try setting the following


[2]. For the error, "tensorflow/stream_executor/platform/default/] Could not load dynamic library ''; dlerror: cannot open shared object file: No such file or directory

I had done

ln -s

and set LIB PATH

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.2/targets/x86_64-linux/lib

Additional Reading

  • U-Net: Convolutional Networks for Biomedical Image Segmentation, Olaf Ronneberger, Philipp Fischer, and Thomas Brox, 2015, arXiv:1505.04597v1 [cs.CV]

  • Horovod Trouble Shooting -


Obtuvo 0 de 5 estrellas.
Aún no hay calificaciones

Agrega una calificación
bottom of page