You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: This is the first paper that presents a multi-column convolutional network for semantic segmentation.

Keyphrases: semantic segmentation images

Dataset description:
MLFP: The MLFP dataset consists of face presentation attacks captured with seven 3D latex masks and three 2D print attacks. The dataset contains videos captured from color, thermal and infrared channels.
Cityscapes: Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void).
MSeg: A composite dataset that unifies semantic segmentation datasets from different domains.
HIGGS Data Set: The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator.
COCO: The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
BraTS 2017: The BRATS2017 dataset. It contains 285 brain tumor MRI scans, with four MRI modalities as T1, T1ce, T2, and Flair for each scan.

Options:- A: MLFP  B: Cityscapes  C: MSeg  D: HIGGS Data Set  E: COCO  F: BraTS 2017
["Cityscapes", "COCO"]