Given a short query with keyphrases about a research idea in 2019 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: A fast approximation algorithm for image segmentation using a heuristic continuous relaxation of the isoperimetric graph partitioning.

Keyphrases: image segmentation images

Dataset description:
BSDS500: Berkeley Segmentation Data Set 500 (BSDS500) is a standard benchmark for contour detection. This dataset is designed for evaluating natural edge detection that includes not only object contours but also object interior boundaries and background boundaries.
DeepScores: DeepScores contains high quality images of musical scores, partitioned into 300,000 sheets of written music that contain symbols of different shapes and sizes. For advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding.
FRGC: The data for FRGC consists of 50,000 recordings divided into training and validation partitions. The training partition is designed for training algorithms and the validation partition is for assessing performance of an approach in a laboratory setting.
MUTAG: In particular, MUTAG is a collection of nitroaromatic compounds and the goal is to predict their mutagenicity on Salmonella typhimurium. Input graphs are used to represent chemical compounds, where vertices stand for atoms and are labeled by the atom type (represented by one-hot encoding), while edges between vertices represent bonds between the corresponding atoms.

Options:- A: BSDS500  B: DeepScores  C: FRGC  D: MUTAG
BSDS500