Skip to main content

A PyTorch based package for automated OCT tissue masking.

Project description

oct_tissuemasking

This package contains a basic 3D UNet and patching scripts to create training data.

1 Installation

1.1 Create a new mamba environment

Create a new mamba environment called oct_tissuemasking with python 3.9.

mamba create -n oct_tissuemasking python=3.9
mamba activate oct_tissuemasking

1.2 Install dependencies

We will need to install synthspline for vasculature synthesis.

pip install git+https://github.com/balbasty/synthspline.git#f78ba23

1.3 Set cuda parameters

We need to identify and set our cuda version to make sure we install the right prebuilt wheel for cupy.

export CUDA_VERSION=<cuda-version>

1.4 Install oct_tissuemasking

This will take a while so we will set the timeout for a 20,000 seconds!

pip install oct_tissuemasking --default-timeout=20000

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

oct_tissuemasking-0.0.8.tar.gz (434.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

oct_tissuemasking-0.0.8-py3-none-any.whl (434.8 kB view details)

Uploaded Python 3

File details

Details for the file oct_tissuemasking-0.0.8.tar.gz.

File metadata

  • Download URL: oct_tissuemasking-0.0.8.tar.gz
  • Upload date:
  • Size: 434.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for oct_tissuemasking-0.0.8.tar.gz
Algorithm Hash digest
SHA256 23e4bbc108fa3bec6faafa911ab4ade53883122dd6386bea9e6ec87017f101aa
MD5 24990f4d70f5728cd74746e06766cea2
BLAKE2b-256 e0c22302b68647d02eda233920005b12c6ea0e00887c451f5f21d2cbe9878174

See more details on using hashes here.

File details

Details for the file oct_tissuemasking-0.0.8-py3-none-any.whl.

File metadata

File hashes

Hashes for oct_tissuemasking-0.0.8-py3-none-any.whl
Algorithm Hash digest
SHA256 3f854dc7d1304ed9bb22cac4797270477dbf187dabc6252cd7363ad0d49c9533
MD5 42f73cadca151b7db7cfe7fb547cf48a
BLAKE2b-256 097cb6bd7fd4c727e99e785dc17a3927d372d71d2e13f8d6088a0e69132e353d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page