Surgical AI Learns to Adapt When Smoke and Darkness Blur the Robot’s View

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ve blends current-frame entropy minimization with cross-frame temporal consistency, weighted at 0.7 and 0.5 respectively. Gradient updates touch only the affine scale and bias parameters of Batch Normalization layers, roughly 0.77 percent of the network’s 3.2 million parameters, while convolutional weights remain frozen. The evaluation protocol is a controlled stress test of unusual rigor. Using the EndoVis 2017 robo

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Robot-assisted surgery depends on a camera that never blinks, but the images it returns are anything but stable. During a minimally invasive procedure, smoke from energy devices drifts across the field, blood pools over tissue, illumination dims, and defocus blur softens instrument edges. A deep learning model trained to segment surgical instruments can perform beautifully on clean validation frames and then fall apa

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tic instrument segmentation dataset, the team generated twelve degradation conditions from the same 901 validation frames: blood interference, imaging blur, low illumination, and smoke, each at three severity levels with explicitly reported digital generation parameters and a deterministic per-frame seeding scheme so that all five compared methods faced identical degraded files. The methods spanned the fixed Source m

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odel, a Batch Normalization statistics baseline, the continual entropy-minimization method TENT, a selective entropy variant with the same uncertainty gate, and the full LiteST-TTA pipeline. The headline numbers are strikingly close. TENT achieved the highest macro-average Dice similarity coefficient of 0.8881 across the twelve conditions, with LiteST-TTA at 0.8879, while the fixed Source model trailed at 0.8533. But

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the condition-wise breakdown reveals a far more interesting story. Under severe low illumination, the Source model collapsed to a DSC of 0.369, essentially failing to segment the instruments, while LiteST-TTA recovered to 0.872, an absolute improvement of more than half a Dice point. Under strong blur, a smaller but real benefit emerged, with adaptation overtaking the Source model only at the highest severity. Under

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ds evolved wildly different update rates, 81 percent versus 23 percent, so the difference could not be attributed to the temporal term. When both methods were forced to update exactly the same frames on a fixed periodic schedule, their macro-average DSC values became virtually identical: 0.886967 versus 0.886966, with a sequence-level test giving p equals 0.496 and an effect size effectively zero. The apparent advant

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