Force boolean column to category while reading a csv
#### Problem description

Reading a csv file with dtypes specified in a dictionary produces NaNs for boolean columns read as category. It seems the output of dtypes changes from version 0.20 to 0.21 so that the below code produces NaNs for the second column. 

This might not really be a bug but it seems quite unintuitive to me as I would expect that output of df.dtypes should be useable to read it correctly again and I would expect the interpretation of booleans as a category as valid.

#### Code to reproduce the issue
```python
b = {'a':[5,4,3,2], 'b':[True, False, True, True], , 'c':['A', 'B', 'A', 'C']}
df = pd.DataFrame(b)

df['b'] = df['b'].astype('category')
df['c'] = df['c'].astype('category')
dtypes_dict = df.dtypes.to_dict()
df.to_csv("data.csv", index=False)

df2 = pd.read_csv("data.csv", dtype=dtypes_dict)
print(df2)
```

#### Produced Output
```
   a    b  c
0  5  NaN  A
1  4  NaN  B
2  3  NaN  A
3  2  NaN  C
```

#### Expected Output
```
   a    b  c
0  5  True  A
1  4  False  B
2  3  True  A
3  2  True  C
```
#### Output of df.dtypes version 0.22
```{'a': dtype('int64'), 'b': CategoricalDtype(categories=[False, True], ordered=False), 'c': CategoricalDtype(categories=['A', 'B', 'C'], ordered=False)}```

#### Output of df.dtypes version 0.20
```{'a': dtype('int64'), 'b': category, 'c': category}```

#### Version
Pands version 0.22
<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.6-040806-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_IE.UTF-8
LOCALE: en_IE.UTF-8

pandas: 0.22.0
pytest: None
pip: 9.0.2
setuptools: 39.0.1
Cython: None
numpy: 1.14.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.0
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
