diff --git a/Paper/bibliography.bib b/Paper/bibliography.bib
index aa683aa..98fda44 100644
--- a/Paper/bibliography.bib
+++ b/Paper/bibliography.bib
@@ -92,7 +92,7 @@ @incollection{Kikinis:2013
 
 @book{d'Albenzio:2023, type={preprint},
   title={Patient-Specific Functional Liver Segments based on Centerline Classification of the Hepatic and Portal Veins},
-  url={https://www.researchsquare.com/article/rs-3574517/v1},
+  url={https://www.researchsquare.com/article/rs-3574517/v2},
   DOI={10.21203/rs.3.rs-3574517/v1},
   abstractNote={Couinaud’s liver segment classification has served as the standard basis for liver surgery planning for nearly seven decades. While providing a systematic framework by dividing the liver into eight segments, its reliance on fixed planar boundaries may not always align with individual liver anatomical variations. In this study, we propose a new method for classifying liver functional segments. By integrating patient-specific liver morphology, 3D vascular system, and user-defined landmarks, our approach offers greater flexibility in classifying the liver while respecting individual anatomical variations. We conducted a comprehensive assessment of our method, comparing it with two widely used liver classification techniques: conventional plane-based and portal vein-based classifications. Our results demonstrate that our method’s flexibility extends beyond conventional software. By enabling the inclusion of both hepatic and portal veins, including peripheral branches, our approach deviates from the classical Couinaud classification. Importantly, our findings indicate that our approach not only overcomes the limitations of traditional methods but also provides a more precise and surgery-ready definition of liver segments, particularly in complex cases involving segments 5 and 8. Furthermore, feedback from liver surgery specialists highlights its potential benefits, including improved visualization in complex cases and better assessment of vascular perfusion territories.},
   institution={In Review},
@@ -151,12 +151,6 @@ @misc{3DSlicerExtensionsManager:2024
   year={2024}
 }
 
-@article{meng2023resectograms,
-  title={Resectograms: Real-Time 2D Visualization of Liver Virtual Resections},
-  author={Meng, Ruoyan and Aghayan, Davit and Pelanis, Egidijus and Edwin, Bj{\o}rn and Cheikh, Faouzi Alaya and Palomar, Rafael},
-  year={2023}
-}
-
 @article{Bismuth:1982,
   title={Surgical anatomy and anatomical surgery of the liver},
   author={Bismuth, Henri},
diff --git a/Paper/paper.md b/Paper/paper.md
index c43ff58..92cd577 100644
--- a/Paper/paper.md
+++ b/Paper/paper.md
@@ -57,13 +57,13 @@ Slicer-Liver is a 3D Slicer extension that can be installed directly through the
 Slicer-Liver has integrated different geometric modeling techniques to model virtual resetions: Deformable Bézier with planar initialization [@Palomar:2017], NURBS (Non-Uniform Rational B-Spline) and contour-initialized resections. The implementation of these methods include visualization of the resection margin, adaptive grid visualization and clipping of the resection excess. Thanks to the computation of distance maps between anatomical structures, resection margin visualization operates in real-time as the user modifies the resection.
 
 **Resectograms**
-As an additional visualization object, Slicer-Liver implements the use of resectograms [@meng2023resectograms], which benefit from the computation of distance maps to extend the real-time visualization of the information along the resection trajectory. Resectograms help the user understand information subject to occlusions in the 3D Scene, as well as serve as an indicator of validity of resections (e.g., making it possible to detect malformed resections or violations of margins).
+As an additional visualization object, Slicer-Liver implements the use of resectograms [@Meng:2023], which benefit from the computation of distance maps to extend the real-time visualization of the information along the resection trajectory. Resectograms help the user understand information subject to occlusions in the 3D Scene, as well as serve as an indicator of validity of resections (e.g., making it possible to detect malformed resections or violations of margins).
 
 **Liver Segments Classification**
 Slicer-Liver leverages the method for computing liver vascular territories and segments classification utilized in [@{d'Albenzio:2023}]. The method uses the liver morphology, the interior vascular network, and user-defined landmarks to provide enhanced flexibility in marker placement, distinguishing it from other methods. One of the advantages of this approach is that vascular territories participated by both portal and hepatic vessel systems can be computed. The liver segments computed can be visualized and processed as segmentation objects or 3D models, by the rest of the tools provided in 3D Slicer.
 
 **Resection Volumetry**
-In the same line as for the liver segments classification, Slicer-Liver implements a tool for marking liver regions (with and without consideration to resections or liver segments) in a way that allows the user to perform liver resection volumetry analysis.
+Slicer-Liver offers a versatile tool for liver resection volumetry analysis, allowing the integration of resection plans with liver parenchyma and liver segments data. Users can interactively select and calculate the volumes of individual or combined regions of interest (ROIs).
 
 # Preliminary results