Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 - 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part IX
Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 - 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part IX
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医学图像计算和计算机辅助干预 - MICCAI 2023 - 第 26 届国际会议，加拿大不列颠哥伦比亚省温哥华，2023 年 10 月 8-12 日，会议记录，第九部分
DOI：
10.1007/978-3-031-43996-4_45
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Das A
中科院分区：
文献类型：
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作者：
Das A
垂体肿瘤位于身体的解剖致密区，常扭曲或包围周围的关键结构。这一点，再加上内窥镜技术带来的解剖变化和限制，使得术中对这些结构的识别和保护具有挑战性。机器学习的进步使得在手术视频中自动识别这些解剖结构成为可能。然而，据作者所知，在垂体内窥镜手术的后期，这仍然是一个未解决的问题。本文提出了一个能够识别10个关键解剖结构的多任务网络——垂体解剖识别网络（PAINet）。PAINet共同学习：(1)对两个最突出、最大、最频繁出现的结构（鞍窝和斜坡隐窝）进行语义分割；(2)其余8个不太突出、较小和不太频繁出现的结构的质心检测。PAINet使用了一个EfficientNetB3编码器和一个U-Net++解码器，带有卷积层用于分割，池化层用于检测。记录了64个视频（635张图像）的数据集，并通过多轮专家共识对解剖结构进行了注释。通过5倍交叉验证，PAINet在sella和clival隐窝语义分割上分别达到66.1%和54.1%的IoU，在其余8个结构的质心检测上达到53.2%的mpcc -20%，提高了单任务性能。因此，这证明了在垂体内镜手术鞍期自动识别解剖关键结构是可能的。
Pituitary tumours are in an anatomically dense region of the body, and often distort or encase the surrounding critical structures. This, in combination with anatomical variations and limitations imposed by endoscope technology, makes intra-operative identification and protection of these structures challenging. Advances in machine learning have allowed for the opportunity to automatically identifying these anatomical structures within operative videos. However, to the best of the authors’ knowledge, this remains an unaddressed problem in the sellar phase of endoscopic pituitary surgery. In this paper, PAINet (Pituitary Anatomy Identification Network), a multi-task network capable of identifying the ten critical anatomical structures, is proposed. PAINet jointly learns: (1) the semantic segmentation of the two most prominent, largest, and frequently occurring structures (sella and clival recess); and (2) the centroid detection of the remaining eight less prominent, smaller, and less frequently occurring structures. PAINet utilises an EfficientNetB3 encoder and a U-Net++ decoder with a convolution layer for segmentation and pooling layer for detection. A dataset of 64-videos (635 images) were recorded, and annotated for anatomical structures through multi-round expert consensus. Implementing 5-fold cross-validation, PAINet achieved 66.1% and 54.1% IoU for sella and clival recess semantic segmentation respectively, and 53.2% MPCK-20% for centroid detection of the remaining eight structures, improving on single-task performances. This therefore demonstrates automated identification of anatomical critical structures in the sellar phase of endoscopic pituitary surgery is possible.