No abstract

Breast reconstruction patients face unique challenges in finding a properly fitting bra after breast surgery, leading to decreased bra comfort and psychosocial functioning. In addition to considerations such as location of seams and choice of fabric, identifying trends in how breast shape and symmetry measurements change between native breasts and reconstructed breasts may help inform bra design for reconstruction patients. We have previously developed a correspondence system between bra measurements and clinical breast measurements used by reconstructive surgeons. The selected measurements describe the size and projection of the breasts as well as their relative location on the torso as captured by clinical photographs. In this study, we explore how reconstruction changes breast measurements pertinent to bra design by analyzing 3D surface torso images of 15 unilateral implantbased breast reconstruction patients before and after their reconstruction surgery. Using custom software developed at University of Houston, two researchers measured several breast properties on a 3D surface torso image taken before mastectomy and on another image taken after each patient's final implants had been placed. 14 of the 15 patients had completed their reconstruction surgeries by the time that the second image was taken. We compared the differences in measurements between the pre-mastectomy image and post-mastectomy and reconstruction image for both breasts separately, as well as change in right-left symmetry of the measurements between the pre-and post-images. 14 out of the 15 patients had a revision surgery performed on the contralateral breast to enhance postreconstruction symmetry. The three most affected measurements between native breasts and breasts reconstructed after mastectomy were the sternal notch to most projecting point, lateral point to most projecting point, and mid-clavicle to transition point to most projecting point. These changes can be attributed to the size and shape of the implants used to compensate for the removal of native breast tissue, which change the fundamental footprint and curvature of the breast. Asymmetry between the breasts also increased after breast cancer treatment despite reconstruction and contralateral revision procedures. These measurements can be used to inform bra designers of what adjustments may be needed to bra patterns to improve fit for reconstruction patients.

Purpose: Saliency models that predict observers' visual attention to facial differences could enable psychosocial interventions to help patients and their families anticipate staring behaviors. The purpose of this study was to assess the ability of existing saliency models to predict observers' visual attention to acquired facial differences arising from head and neck cancer and its treatment.Approach: Saliency maps predicted by graph-based visual saliency (GBVS), an artificial neural network (ANN), and a face-specific model were compared to observer fixation maps generated from eye-tracking of lay observers presented with clinical facial photographs of patients with a visible or functional impairment manifesting in the head and neck region. We used a linear mixed-effects model to investigate observer and stimulus factors associated with the saliency models' accuracy.Results: The GBVS model predicted many irrelevant regions (e.g., shirt collars) as being salient. The ANN model underestimated observers' attention to facial differences relative to the central region of the face. Compared with GBVS and ANN, the face-specific saliency model was more accurate on this task; however, the face-specific model underestimated the saliency of deviations from the typical structure of human faces. The linear mixed-effects model revealed that the location of the facial difference (midface versus periphery) was significantly associated with saliency model performance. Model performance was also significantly impacted by interobserver variability.Conclusions: Existing saliency models are not adequate for predicting observers' visual attention to facial differences. Extensions of face-specific saliency models are needed to accurately predict the saliency of acquired facial differences arising from head and neck cancer and its treatment.