3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in
  Radiology
Asma Ben Abacha Alberto Santamaria-Pang Ho Hin Lee Jameson Merkow Qin Cai Surya Teja Devarakonda Abdullah Islam Julia Gong Matthew P. Lungren Thomas Lin Noel C Codella Ivan Tarapov
The increasing use of medical imaging in healthcare settings presents a
significant challenge due to the increasing workload for radiologists, yet it
also offers opportunity for enhancing healthcare outcomes if effectively
leveraged. 3D image retrieval holds potential to reduce radiologist workloads
by enabling clinicians to efficiently search through diagnostically similar or
otherwise relevant cases, resulting in faster and more precise diagnoses.
However, the field of 3D medical image retrieval is still emerging, lacking
established evaluation benchmarks, comprehensive datasets, and thorough
studies. This paper attempts to bridge this gap by introducing a novel
benchmark for 3D Medical Image Retrieval (3D-MIR) that encompasses four
different anatomies imaged with computed tomography. Using this benchmark, we
explore a diverse set of search strategies that use aggregated 2D slices, 3D
volumes, and multi-modal embeddings from popular multi-modal foundation models
as queries. Quantitative and qualitative assessments of each approach are
provided alongside an in-depth discussion that offers insight for future
research. To promote the advancement of this field, our benchmark, dataset, and
code are made publicly available.