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@@ -185,6 +185,7 @@ <h2 class="title is-3 publication-title">SIMBA: Specific Identity Markers for Bo
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             <span class="author-block"><sup>*</sup>Denotes equal contribution.</span> 
+            <span class="author-block">Poster at MICCAI 2020</span> 
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@@ -243,7 +244,79 @@ <h2 class="title is-3 publication-title">SIMBA: Specific Identity Markers for Bo
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+        <div class="column has-text-centered">
+          <h2 class="title is-3 publication-title">Hand Pose Estimation for Pediatric Bone Age Assessment</h2>
+          <div class="is-size-5 publication-authors">
+            <span class="author-block">Maria Escobar<sup>*1</sup>,</span>
+            <span class="author-block">Cristina González<sup>*1</sup>,</span>
+            <span class="author-block">Felipe Torres<sup>1</sup>,</span>
+            <span class="author-block">Laura Daza<sup>1</sup>,</span>
+            <p></p>
+            <span class="author-block">Gustavo Triana<sup>2</sup>,</span>
+            <span class="author-block">Pablo Arbeláez<sup>1</sup>,</span>
+          </div>
+          <div class="is-size-5 publication-authors">
+            <span class="author-block"><sup>1</sup>CINFONIA, Universidad de Los Andes, </span>
+            <span class="author-block"><sup>2</sup>Fundación Santa Fé de Bogotá </span>         
+          </div>
+           <div class="is-size-5 publication-authors">
+            <span class="author-block"><sup>*</sup>Denotes equal contribution.</span> 
+            <span class="author-block">Oral presentation at MICCAI 2019</span> 
+          </div>
+
+          <div class="column has-text-centered">
+            <div class="publication-links">
+              <!-- PDF Link. -->
+              <span class="link-block">
+                <a href="https://link.springer.com/chapter/10.1007/978-3-030-32226-7_59"
+                   class="external-link button is-normal is-rounded is-dark">
+                  <span class="icon">
+                      <i class="fas fa-file-pdf"></i>
+                  </span>
+                  <span>Paper</span>
+                </a>
+              </span>
+              <span class="link-block">
+                <a href="https://cinfonia.uniandes.edu.co/wp-content/uploads/2021/06/Hand_Pose_Estimation_for_Pediatric_Bone_Age_Assessment.pdf"
+                   class="external-link button is-normal is-rounded is-dark">
+                  <span class="icon">
+                      <i class="ai ai-arxiv"></i>
+                  </span>
+                  <span>arXiv</span>
+                </a>
+              </span>
+              <!-- Code Link. -->
+              <span class="link-block">
+                <a href="https://github.com/BCV-Uniandes/Bonet"
+                   class="external-link button is-normal is-rounded is-dark">
+                  <span class="icon">
+                      <i class="fab fa-github"></i>
+                  </span>
+                  <span>Code</span>
+                  </a>
+              </span>
+            </div>
 
+          </div>
+          <div class="content has-text-justified">
+            <p>
+              We present a new experimental framework for the task of Bone Age Assessment (BAA) based on a local
+              analysis of anatomical Regions Of Interest (ROIs) of hand radiographs. For this purpose, we introduce the
+              Radiological Hand Pose Estimation (RHPE) Dataset, composed of 6,288 hand radiographs from a
+              population that is different from the currently available BAA datasets. We provide Bone Age groundtruths
+              annotated by two expert radiologists as well as bounding boxes and keypoints denoting anatomical ROIs
+              annotated by multiple trained subjects. In addition to RHPE, we provide bounding boxes and ROIs
+              annotations for the publicly available BAA dataset by the Radiological Society of North America (RSNA). We propose a new experimental framework with hand detection and hand pose estimation as new tasks
+              to extract local information for BAA methods. Thanks to its fine-grained and precisely localized
+              annotations, our dataset will allow to exploit local information to push forward automated BAA
+              algorithms. Additionally, we conduct experiments with state-of-the-art methods in each of the new tasks.
+              Our proposed model, named BoNet, leverages local information and significantly outperforms state-of-the-
+              art methods in BAA. We provide the RHPE dataset with the corresponding annotations, as well as the
+              trained models, the source code for BoNet and the additional annotations created for the RSNA dataset.
+            </p>
+
+          </div>
+        </div>
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