Statistical Shape Models for the Planning of TKA Surgery

5 pages•Published: January 5, 2026

Anna Gounot, Marion Decrouez, Baptiste Dehaine, Guillaume Dardenne and Valérie Burdin

Abstract

CT-based methods, such as robotic systems and patient-specific instrumentation (PSI), offer precise bone depiction, making them valuable for Total Knee Arthroplasty (TKA). However, they must be robust to the presence of cartilage, which is not easily visible on CT-scans. We present here a coupled bone and cartilage Statistical Shape Model (SSM) that predicts cartilage solely from bone shape. Four models were trained and tested for healthy and pathological patients, for both femur and tibia. Cartilage prediction results show good adaptability to the pathology as well as similar accuracy compared to the inter-observer MRI manual segmentation variability. This solution could be integrated in the planning of TKA surgeries to improve CT-based PSI and robotic systems.

Keyphrases: bone, cartilage, osteoarthritis, patient specific modeling, statistical shape model, total knee arthroplasty

In: Joshua William Giles and Aziliz Guezou-Philippe (editors). Proceedings of The 25th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 8, pages 98-102.

Links:	https://easychair.org/publications/paper/cbqB
	https://doi.org/10.29007/9k5h

BibTeX entry

@inproceedings{CAOS2025:Statistical_Shape_Models_Planning,
  author    = {Anna Gounot and Marion Decrouez and Baptiste Dehaine and Guillaume Dardenne and Valérie Burdin},
  title     = {Statistical Shape Models for the Planning of TKA Surgery},
  booktitle = {Proceedings of The 25th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Joshua William Giles and Aziliz Guezou-Philippe},
  series    = {EPiC Series in Health Sciences},
  volume    = {8},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {/publications/paper/cbqB},
  doi       = {10.29007/9k5h},
  pages     = {98-102},
  year      = {2026}}

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