Automatic parsing of human anatomies at instance-level from 3D computed tomography (CT) scans is a prerequisite step for many clinical applications. The presence of pathologies, broken structures or limited field-of-view (FOV) all can make anatomy parsing algorithms vulnerable. In this work, we explore how to exploit and conduct the prosperous detection-then-segmentation paradigm in 3D medical data, and propose a steerable, robust, and efficient computing framework for detection, identification, and segmentation of anatomies in CT scans. Considering complicated shapes, sizes and orientations of anatomies, without lose of generality, we present the nine degrees-of-freedom (9-DoF) pose estimation solution in full 3D space using a novel single-stage, non-hierarchical forward representation. Our whole framework is executed in a steerable manner where any anatomy of interest can be directly retrieved to further boost the inference efficiency. We have validated the proposed method on three medical imaging parsing tasks of ribs, spine, and abdominal organs. For rib parsing, CT scans have been annotated at the rib instance-level for quantitative evaluation, similarly for spine vertebrae and abdominal organs. Extensive experiments on 9-DoF box detection and rib instance segmentation demonstrate the effectiveness of our framework (with the identification rate of 97.0% and the segmentation Dice score of 90.9%) in high efficiency, compared favorably against several strong baselines (e.g., CenterNet, FCOS, and nnU-Net). For spine identification and segmentation, our method achieves a new state-of-the-art result on the public CTSpine1K dataset. Last, we report highly competitive results in multi-organ segmentation at FLARE22 competition. Our annotations, code and models will be made publicly available at: https://github.com/alibaba-damo-academy/Med_Query.
翻译:从 3D 计算断层仪(CT) 扫描中自动解剖人体解剖仪,这是许多临床应用的一个必要步骤。 病理学、 断层结构或有限的视域( FOV) 的存在, 都会使解剖解析算法变得脆弱。 在这项工作中, 我们探索如何在 3D 医学数据中探索和进行繁荣的检测- 即时分解模式。 并提议一个可控、 稳健、 高效的计算框架, 用于检测、 识别和分解 CT 扫描中的解剖仪。 考虑到 解剖解析的复杂形状、 大小和方向, 而不会失去一般性, 我们展示9- 自由度( 9- DoF) 的9 度估算解决方案在全3D 空间里, 使用一个新的单阶段、 非高度的前瞻性前方表示。 我们的整个框架将以一种可控的方式执行, 任何具有高度竞争力的解析度, 以进一步提升电算效率。 我们已经验证了三个医学成像级的内断断层 、 脊椎、 和直角断断断断断断断断断断断断断断断断断断断断断断断层机、 、 、 度 等 的 数据在 O、 上, 在直判、 直判、 直判、 直判、 直判、 直判、 直判、 直判、 直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判、直判