Volumetric Medical Image Segmentation

The PROMISE12 dataset was made available for the MICCAI 2012 prostate segmentation challenge. Magnetic Resonance (MR) images (T2-weighted) of 50 patients with various diseases were acquired at different locations with several MRI vendors and scanning protocols. Source: Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions Image Source: https://promise12.grand-challenge.org/

A total of 227 cross sectional images (20 x 54 mm with a resolution of 289 x 648 pixels) of hind-leg xenograft tumors from 29 mice were obtained with 1mm step-wise movement of the array mounted on a manual positioning device. The whole tumor volume was acquired using a diagnostic ultrasound system with a 10 MHz linear transducer and 50 MHz sampling. The dataset images are in Analyze 7.5 file format consisting of two files: a header with information about the size and number of voxels in each dimension (filename extension .hdr), and the actual data in binary format (filename extension .img). The images in the dataset are formatted according to the following protocol: CASE NUMBER | FILE TYPE (LOC OR SEG) | FILE EXTENSION | where loc is the original image and seg is the associated segmentation mask. The volumetric data are categorised into progressive versus non-progressive cases in response to chemotherapy treatment. The example_case folder contains an output of the 3D Multi-fractal Nakagami Feature descriptor (MNF) algorithm when applied to one of the liver tumour cases, see below. The group of slice images for a particular case where transformed to corresponding Nakagami shape and scale parametric images, and animated GIF images representing the fractal slice maps and a video of the corresponding fractal volume maps were estimated and included as well.

Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases. This dataset consists of 413 real clinical CT scans and 413x9 MR scans, all 19 organs were annotated by a senior oncologist (MD. Wenjun Liao, 10 years experiment). some challenging cases in clinical practice can help evaluate the generalization and robustness of deep learning methods. Some organ annotations not present in previous public datasets (such as prostate, seminal vesicles, etc).