The Dresden Surgical Anatomy Dataset for Abdominal Organ Segmentation in Surgical Data Science

Abstract

Laparoscopy is an imaging technique that enables minimally-invasive procedures in various medical disciplines including abdominal surgery, gynaecology and urology. To date, publicly available laparoscopic image datasets are mostly limited to general classifications of data, semantic segmentations of surgical instruments and low-volume weak annotations of specific abdominal organs. The Dresden Surgical Anatomy Dataset provides semantic segmentations of eight abdominal organs (colon, liver, pancreas, small intestine, spleen, stomach, ureter, vesicular glands), the abdominal wall and two vessel structures (inferior mesenteric artery, intestinal veins) in laparoscopic view. In total, this dataset comprises 13195 laparoscopic images. For each anatomical structure, we provide over a thousand images with pixel-wise segmentations. Annotations comprise semantic segmentations of single organs and one multi-organ-segmentation dataset including segments for all eleven anatomical structures. Moreover, we provide weak annotations of organ presence for every single image. This dataset markedly expands the horizon for surgical data science applications of computer vision in laparoscopic surgery and could thereby contribute to a reduction of risks and faster translation of Artificial Intelligence into surgical practice.

Artificial Intelligence for context-aware surgical guidance in complex robot-assisted oncological procedures: An exploratory feasibility study

“…Previous studies investigating segmentation of anatomical structures have mostly focused on classification of the presence of certain organs [15] or rough localization of the detected organs using bounding boxes [33]. A recent preprint [12] describes semantic segmentation of organs based on a large-scale dataset of organ segmentations [13]. This preprint covers six organs that were also analyzed in the present work.…”

“…Thus far, translational Artificial Intelligence (AI)-based success stories in the field of surgery are lacking and clinical applications are mostly limited to orthopedic, neurosurgical, and hepatic surgical procedures [8,9]. With regard to approaches with high translational potential in laparoscopic surgery, deep learning-based algorithms have recently been shown to identify relevant anatomical areas during cholecystectomy [10,11] and organs during laparoscopy [12,13]. Of note, such methods have primarily been established for less complex surgical procedures, for which – in part – open-access datasets exist for the purpose of algorithm development and validation [14,15].…”

Anatomy Segmentation in Laparoscopic Surgery: Comparison of Machine Learning and Human Expertise – An Experimental Study

“…Based on the full-length surgery recordings and respective temporal annotations of organ visibility, individual image frames were extracted and annotated as described previously. 22 The resulting Dresden Surgical Anatomy Dataset comprises 13195 distinct images with pixel-wise segmentations of eleven anatomical structures: abdominal wall, colon, intestinal vessels (inferior mesenteric artery and inferior mesenteric vein with their subsidiary vessels), liver, pancreas, small intestine, spleen, stomach, ureter and vesicular glands. Moreover, the dataset comprises binary annotations of the presence of each of these organs for each image.…”

“…All participants provided written informed consent to anonymous study participation, data acquisition and analysis, and publication. In total, 28 participants (physician and non-physician medical staff, medical students, and medical laypersons) marked the pancreas in 35 images from the Dresden Surgical Anatomy Dataset 23 with bounding boxes. These images originated from 26 different surgeries, and the pancreas was visible in 16 of the 35 images.…”

“…To advance and diversify the applications of computer vision in laparoscopic surgery, we have recently published the Dresden Surgical Anatomy Dataset 23 , providing 13195 laparoscopic images with high-quality annotations of the presence and exact location of eleven intraabdominal anatomical structures: abdominal wall, colon, intestinal vessels (inferior mesenteric artery and inferior mesenteric vein with their subsidiary vessels), liver, pancreas, small intestine, spleen, stomach, ureter and vesicular glands. Here, we present the first study evaluating automated Anatomy Segmentation in Laparoscopic Surgery detection and localization of organs and anatomical structures in laparoscopic view based on this dataset, and, using the example of delineation of the pancreas, compare algorithm performance to that of humans.…”