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With the help of an intra-operative mobile C-arm CT, medical interventions can be verified and corrected, avoiding the need for a post-operative CT and a second intervention. An exact adjustment of standard plane positions is necessary for the best possible assessment of the anatomical regions of interest but the mobility of the C-arm causes the need for a time-consuming manual adjustment. In this article, we present an automatic plane adjustment at the example of calcaneal fractures. We developed two feature detection methods (2D and pseudo-3D) based on SURF key points and also transferred the SURF approach to 3D. Combined with an atlas-based registration, our algorithm adjusts the standard planes of the calcaneal C-arm images automatically. The robustness of the algorithms is evaluated using a clinical data set. Additionally, we tested the algorithm’s performance for two registration approaches, two resolutions of C-arm images and two methods for metal artifact reduction. For the feature extraction, the novel 3D-SURF approach performs best. As expected, a higher resolution ($$512^3$$ 5123 voxel) leads also to more robust feature points and is therefore slightly better than the $$256^3$$ 2563 voxel images (standard setting of device). Our comparison of two different artifact reduction methods and the complete removal of metal in the images shows that our approach is highly robust against artifacts and the number and position of metal implants. By introducing our fast algorithmic processing pipeline, we developed the first steps for a fully automatic assistance system for the assessment of C-arm CT images.

During a standard fracture reduction and fixation procedure of the distal radius, only fluoroscopic images are available for planning of the screw placement and monitoring of the drill bit trajectory. Our prototype intra-operative framework integrates planning and drill guidance for a simplified and improved planning transfer. Guidance information is extracted using a video camera mounted onto a surgical drill. Real-time feedback of the drill bit position is provided using an augmented view of the planning X-rays. We evaluate the accuracy of the placed screws on plastic bones and on healthy and fractured forearm specimens. We also investigate the difference in accuracy between guided screw placement versus freehand. Moreover, the accuracy of the real-time position feedback of the drill bit is evaluated. A total of 166 screws were placed. On 37 plastic bones, our obtained accuracy was $$1.01\,\pm \,0.56$$ 1.01±0.56 mm, $$3.74^\circ \,\pm \,4.39^\circ $$ 3.74∘±4.39∘ and $$1.70^\circ \,\pm \,1.35^\circ $$ 1.70∘±1.35∘ in tip position and orientation (azimuth and elevation), respectively. On the three healthy forearm specimens, our obtained accuracy was $$1.63 \pm 0.91$$ 1.63±0.91 mm, $$5.85^\circ \pm 4.93^\circ $$ 5.85∘±4.93∘ and $$3.48^\circ \pm 3.07^\circ $$ 3.48∘±3.07∘ . On the two fractured specimens, we attained: $$1.39 \pm 0.47$$ 1.39±0.47 mm, $$2.93^\circ \pm 1.83^\circ $$ 2.93∘±1.83∘ and $$2.14^\circ \pm 1.84^\circ $$ 2.14∘±1.84∘ . When screw plans were applied freehand (without our guidance system), the achieved accuracy was $$1.73 \pm 0.82$$ 1.73±0.82 mm, $$6.01^\circ \pm 4.94^\circ \,\mathrm{{and}}\, 3.52^\circ \pm 2.48^\circ $$ 6.01∘±4.94∘and3.52∘±2.48∘ , while when they were transferred under guidance, we obtained $$0.89 \pm 0.37$$ 0.89±0.37 mm, $$2.85^\circ \pm 2.57^\circ \,\mathrm{{and}}\, 1.49^\circ \pm 1.17^\circ $$ 2.85∘±2.57∘and1.49∘±1.17∘ . Our results show that our framework is expected to increase the accuracy in screw positioning and to improve robustness w.r.t. freehand placement.

The assessment of intra-operatively acquired volumetric data is a difficult and often time-consuming task, which demands a new set of skills from the surgeons. In the case of orthopedic surgeries such as the treatment of calcaneal fractures, the correctness of the reduction of the bone fragments can be verified with the help of C-arm CT volumetric images. For an accurate intra-operative assessment of the displaced fragments, an automatic segmentation of the articular surfaces and color-coded visualization was developed. Our automatic approach consists of three major steps: first, using adjusted standard planes intersecting the articular region, the joint space is localized with an intensity profile-based method. In a second step, the localized joint space is segmented on the Laplacian of Gaussian filtered volumetric image by a modified binary flood fill algorithm. Finally, a 3D surface model of the segmented joint space is analyzed and visualized with focus on critical displacements of the surface. A specifically designed human cadaver study consisting of ten lower legs of ten different donors was conducted to acquire 48 realistic C-arm CT images of misaligned bone fragments (steps of varying sizes) in the posterior talar articular surface of the calcaneus. The proposed algorithmic pipeline was verified by the acquired image data and showed very good results with no false positives and an overall correct displacement assessment of $$93.8\,\%$$ 93.8% . The proposed algorithmic pipeline can be easily integrated into the clinical workflow and qualifies for intra-operative usage. It showed very good results on the reference data set of the cadaver study. With the help of such an assistance system, the time-consuming process of 2D view adjustment and visual assessment of the gray value images can be greatly simplified.

Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs. Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence. A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was $$0.96\,\hbox {mm} \pm 0.35\,\hbox {mm}$$ 0.96mm±0.35mm , requiring $$<\!5$$ <5 s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference. A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.