DOC: Minor changes and typo fixes

Paper/paper.md

@@ -45,7 +45,7 @@ This article introduces Slicer-Liver, an extension for the 3D Slicer image compu
 
 Liver cancer, both primary and metastatic (e.g., from colorectal cancer), is a global health concern with increasing incidence rates [@Siegel:2023] and surgical resection remains the most potentially curative treatment for some of these patients [@Petrowsky2020]. The evolution of computer-assisted surgical systems over the past two decades has significantly improved tumor localization and surgeons confidence during surgery [@Hansen:2014; @Lamata:2010]. Despite these advances, systematic use of computer-assisted systems for planning liver resections remains a challenge.
 
-Thanks to the latest advances in artificial intelligence, generation of 3D ptient-specific models for surgical planning is increasingly becoming a reality in the clinical routine, however, surgical planning with the use of these models remains a complex and  manual process. Planning of surgery is particularly important for complex cases (e.g., those presenting multiple tumors or those where the location and size of the tumor poses a challenge for the surgery practice). Furthermore, precise surgical planning should not only account for the liver geometry, but also for the blood supply to various liver regions (segments) [@Warmann:2016; @Bismuth:2013]. Furthermore, visualization of 3D liver models and resections a difficult task, where occlusions can prevent the effective understanding of the surgical plan. In addition, there is no broad consensus on the definition of a good resection plan, which is partly due to the lack of formal methods to specify and communicate resection plans, and partly due to the different surgery cultures and practices in different hospitals.
+Thanks to the latest advances in artificial intelligence, generation of 3D patient-specific models for surgical planning is increasingly becoming a reality in the clinical routine. However, surgical planning with the use of these models remains a complex and  manual process. Planning of surgery is particularly important for complex cases (e.g., those presenting multiple tumors or those where the location and size of the tumor poses a challenge for the surgery practice). Furthermore, precise surgical planning should not only account for the liver geometry, but also for the blood supply to various liver regions (segments) [@Warmann:2016; @Bismuth:2013]. Furthermore, visualization of 3D liver models and resections is a difficult task, where occlusions can prevent the effective understanding of the surgical plan. In addition, there is no broad consensus on the definition of a good resection plan, which is partly due to the lack of formal methods to specify and communicate resection plans, and partly due to the different surgery cultures and practices in different hospitals.
 
 All these challenges support the advancing on the integration of computer-aided systems for surgical planning into the clinical reality. In response to these challenges, Slicer-Liver provides functionality for modeling of resections and vascular territories, visualization of virtual resections in lower dimensions and volumetry analysis. In addition, Slicer-Liver serves as a research sandbox for integration of new liver therapy planning methods (e.g., liver ablations) and can be extended to planning of therapies in organs other than the liver.
 
@@ -54,7 +54,7 @@ All these challenges support the advancing on the integration of computer-aided
 Slicer-Liver is a 3D Slicer extension that can be installed directly through the 3D Slicer Extension Manager [@3DSlicerExtensionsManager:2024]. Slicer-Liver is intended for research purposes and is distributed through an MIT license. The software architecture is based on 3D Slicer modules (both C++ and Python) and follows common patterns found in other 3D Slicer extensions. The software provides 4 bodies of functionality that can be used independently or in conjunction as described in the following. 
 
 **Definition of Virtual Resections**
-Slicer-Liver has integrated different geometric modeling techniques to model virtual resetions (deformable Bézier with planar initialization [@Palomar:2017], NURBS 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.
+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 indicator of validity of resections (e.g., making possible detection of malformed resections or violations of margins).
@@ -68,7 +68,7 @@ In the same line as for the liver segments classification, Slicer-Liver implemen
 # Preliminary results
 
 **Definition of Virtual Resections**
-Slicer-Liver provides computer-aided preoperative planning systems, streamlining the resection planning process and introducing real-time 3D cutting path visualization [@Aghayan:2023], shown in \autoref{fig:resesction_specification}. The presented approach empowers users to make informed decisions based on individual patient needs, enhancing outcomes for both atypical and anatomical resections as represented in \autoref{fig:atypical_vs_anatomical}. Notably, the proposed new resection methods aim to obtain better liver remnant preservation compared to existing methods.
+Slicer-Liver provides computer-aided preoperative planning systems, streamlining the resection planning process and introducing real-time 3D cutting path visualization [@Aghayan:2023], shown in \autoref{fig:resection_specification}. The presented approach empowers users to make informed decisions based on individual patient needs, enhancing outcomes for both atypical and anatomical resections as represented in \autoref{fig:atypical_vs_anatomical}. Notably, the proposed new resection methods aim to obtain better liver remnant preservation compared to existing methods.
 
 ![Specification of a virtual resection with visualization of safety margins.\label{fig:resection_specification}](Figures/resection_specification.png)
 
@@ -80,7 +80,7 @@ Adding resectograms to Slicer-Liver, allows a real-time 2D representation of res
 ![Virtual 3D resection with corresponding 2D resectogram.\label{fig:resection_resectogram}](Figures/resectograms_overview.svg){ width=100% }
 
 **Classification of Liver Segments**
-By departing from the standardized Couinaud classification of the liver in segments, the implemented approach our approach enables a more individualized representation of liver segmental distribution \autoref{fig:liver_segments}. Particularly noteworthy is the method’s accurate estimation of the challenging Segment 1, resulting in a comprehensive and precise segmentation of the caudate lobe. While improvements, particularly in automating the landmark marking process, are needed, the approach holds promise for improving liver surgery planning and has the potential to optimize surgical outcomes.
+By departing from the standardized Couinaud classification of the liver in segments, the implemented approach enables a more individualized representation of liver segmental distribution [@{d'Albenzio:2023}] \autoref{fig:liver_segments}. Particularly noteworthy is the method’s accurate estimation of the challenging Segment 1, resulting in a comprehensive and precise segmentation of the caudate lobe. While improvements, particularly in automating the landmark marking process, are needed, the approach holds promise for improving liver surgery planning and has the potential to optimize surgical outcomes.
 
 ![Visualizing liver segments based on annotated hepatic and portal vessel segments around the tumor.\label{fig:liver_segments}](Figures/liver_segments.png)