Knee:

GradICON medium resolution model released: pull request

Update iconregistration pip install iconregistration==0.3.3 and switch to the new model #model = pretrainedmodels.OAIkneesregistrationmodel() model = pretrainedmodels.OAIkneesgradICONmodel() This release runs at half resolution just like the old model, with much fewer folds, better dice, and faster runtime: action with results

Let me know if you also want the full resolution model: it's not quite ready for release right now because it only runs on gpus with at least 20 GB of vram

Lung:

Ideas attempted:

Focus registration on lung instead of ribs

• only evaluate loss in lung region of moving image

• only evaluate loss in lung region of fixed image

• only evaluate loss in intersection of lung segmentations

• Zero image intensity outside of lung region

• Clip image intensity to -1000, 0 before training ✅

Combat overfitting

• Warp both fixed and moving image with same linear transform

• Train on longitudinal as well as cross-subject pairs

• Warp both fixed and moving image with different linear transform ✅

Best (bad) result so far:

mTRE: 9.770269366339061, mTRE_X: 2.2851461818021503, mTRE_Y: 6.378642569335982, mTRE_Z: 5.522105794414662, DICE: 0.9608421929940194
mTRE: 7.972476933755549, mTRE_X: 2.5015854799159594, mTRE_Y: 4.762276590067334, mTRE_Z: 4.4913444717735675, DICE: 0.9781910842486858

Ideas not tested yet:

• LNCC

• Bring Lin's architecture forward

• Do highest resolution training

• Do overfit

notebook

Brain

Task for marc: fix permissions on test set?

Training setup:

2 trainings currently in flight.

Both $\lambda = .05$

Both skull stripped

one at half res, the other progressive starting with quarter res.

notebook

Only visual results so far- evaluate with me?

TODO:

• Evaluate on MindBoggle101

• Evaluate on LPBA40

• Just do the quicksilver evaluation

ICON library tutorial

Working on an ICON library tutorial here

Transformer compatible with GradICON loss?

Not as far as I can tell

Theory TODO

• Hessian of loss

• 1-D experiment

• fewer or more samples

• ICON without gradicon, but with loss barrier around $|J| = 0$

• optimization without network