  Tuberculosis (TB) is an infectious disease caused by Mycobacterium
tuberculosis (Mtb.) that produces pulmonary damage due to its airborne nature.
This fact facilitates the disease fast-spreading, which, according to the World
Health Organization (WHO), in 2021 caused 1.2 million deaths and 9.9 million
new cases. Fortunately, X-Ray Computed Tomography (CT) images enable capturing
specific manifestations of TB that are undetectable using regular diagnostic
tests.
  However, this procedure is unfeasible to process the thousands of volume
images belonging to the different TB animal models and humans required for a
suitable (pre-)clinical trial. To achieve suitable results, automatization of
different image analysis processes is a must to quantify TB. Thus, in this
thesis, we introduce a set of novel methods based on the state of the art
Artificial Intelligence (AI) and Computer Vision (CV). Initially, we present an
algorithm to assess Pathological Lung Segmentation (PLS). Next, a Gaussian
Mixture Model ruled by an Expectation-Maximization (EM) algorithm is employed
to automatically.
  Chapter 3 introduces a model to automate the identification of TB lesions and
the characterization of disease progression.
  Chapter 4 extends the classification of TB lesions. Namely, we introduce a
computational model to infer TB manifestations present in each lung lobe of CT
scans by employing the associated radiologist reports as ground truth. In
Chapter 5, we present a DL model capable of extracting disentangled information
from images of different animal models, as well as information of the
mechanisms that generate the CT volumes.
  To sum up, the thesis presents a collection of valuable tools to automate the
quantification of pathological lungs. Chapter 6 elaborates on these
conclusions.
