def return_images_without_outliers(img_df):
    """ Filter out training images that are known to have bad/invalid masks. """
    outliers = ['7b38c9173ebe69b4c6ba7e703c0c27f39305d9b2910f46405993d2ea7a963b80',
                'adc315bd40d699fd4e4effbcce81cd7162851007f485d754ad3b0472f73a86df',
                '12aeefb1b522b283819b12e4cfaf6b13c1264c0aadac3412b4edd2ace304cb40']

    new_df = img_df.copy()
    for id in range(len(outliers)):
        new_df.drop(new_df[new_df['imageID'] == outliers[id]].index, inplace=True)

    return new_df