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              <creatorName>Zhou, Jinfan</creatorName>
              <givenName>Jinfan</givenName>
              <familyName>Zhou</familyName>
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            <creator>
              <creatorName>Muirhead, William</creatorName>
              <givenName>William</givenName>
              <familyName>Muirhead</familyName>
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            <creator>
              <creatorName>Williams, Simon C</creatorName>
              <givenName>Simon C</givenName>
              <familyName>Williams</familyName>
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            <creator>
              <creatorName>Stoyanov, Danail</creatorName>
              <givenName>Danail</givenName>
              <familyName>Stoyanov</familyName>
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            <creator>
              <creatorName>Marcus, Hani</creatorName>
              <givenName>Hani</givenName>
              <familyName>Marcus</familyName>
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            <creator>
              <creatorName>Mazomenos, Evangelos</creatorName>
              <givenName>Evangelos</givenName>
              <familyName>Mazomenos</familyName>
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          <titles>
            <title><![CDATA[Microsurgical Aneurysm Clipping Surgery (MACS) Dataset with image-level aneurysm presence/absence annotations]]></title>
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          <subjects>
            <subject>Biomedical imaging</subject>
            <subject>aneurysm clipping</subject>
            <subject>aneurysm clipping surgery</subject>
            <subject>Surgical video</subject>
            <subject>Surgical data science</subject>
            <subject>Transformer models</subject>
            <subject>object detection method</subject>
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            <date dateType="Created">2023-07-06</date>
            <date dateType="Updated">2023-07-06</date>
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          <publicationYear>2023</publicationYear>
          <publisher>University College London</publisher>
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            <description descriptionType="Abstract"><![CDATA[<p>We collected and present a microsurgical aneurysm clipping surgery (MACS) dataset comprising of 16 videos of MACS procedures with aneurysm presence/ absence annotations at frame-level (~350k frames) conducted by expert neurosurgeons. We extract frames at 5 fps and establish a dataset of 356,165 images. The MACS dataset is composed of FHD (1920x1080) surgical videos from the operative microscope of 16 patients during surgical repair of intracerebral aneurysms. The study was registered with the hospital’s (National Hospital for Neurology and Neurosurgery, UCLH NHS) local audit committee and data sharing was approved by the information governance lead. All patients provided written informed consent for their video to be collected for research. The MACS dataset was blindly reviewed by two senior vascular neurosurgeons in duplicate. Frames were classified as follows: Type-X : No aneurysm in microscope’s view, Type-Y : Aneurysm in microscope’s view (including both visible and clipped aneurysms), Type-Z: Frame excluded from analysis.  </p>
<p><br></p>]]></description>
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